Text Classification
Transformers
Safetensors
PyTorch
Dutch
modernbert
feature-extraction
multi-label
dutch
municipal-complaints
mbert
bert
Eval Results (legacy)
text-embeddings-inference
Instructions to use UWV/wimbert-synth-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UWV/wimbert-synth-v0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UWV/wimbert-synth-v0")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("UWV/wimbert-synth-v0") model = AutoModel.from_pretrained("UWV/wimbert-synth-v0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update model card
Browse files
README.md
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## License
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Apache 2.0
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---
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Built with Hugging Face Transformers • Trained on UWV WIM synthetic data
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## License
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Apache 2.0
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## Acknowledgements
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This model builds upon:
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- **ModernBERT** by [Answer.AI](https://github.com/AnswerDotAI/ModernBERT) — Modern BERT architecture with efficiency improvements
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- **mmBERT** by Answer.AI & collaborators ([blog](https://huggingface.co/blog/mmbert), [paper](https://arxiv.org/abs/2509.06888)) — Multilingual encoder trained on 1,800+ languages
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The WIM project is funded by the [Innovatiebudget Digitale Overheid (IDO) 2024](https://www.rvo.nl/subsidies-financiering/ido) from the Dutch Ministry of Internal Affairs and Kingdom Relations (BZK).
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---
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Built with Hugging Face Transformers • Trained on UWV WIM synthetic data
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