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")# pip install -U transformers accelerate # 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
Commit History
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Update model card
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Update model card
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Extract DualHeadModel to model.py for inference, update model card
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Update README.md
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Update with model trained on extra dataset
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Add extra dataset, also filter no subtopic from beleving
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Replace no subtopic found with empty list
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Update train script to filter calamity messages
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Update model card
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Rename model card
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Initial commit of WimBERT-synth-v0
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initial commit
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