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
Download tokenizer.json from UWV/wimbert-synth-v0: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/UWV/wimbert-synth-v0/resolve/main/tokenizer.json
- Command line
-
hf download hf://UWV/wimbert-synth-v0/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/UWV/wimbert-synth-v0/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- ee04c6f49e96de036e8a358c46d6d26a0b08f3344d1cb6a712feb09ddbbf9d14
- Size of remote file:
- 34.4 MB
- SHA256:
- b5e0d8cfefee1eb9d19b4a6f301d5ae937cb866ff3d9a3af101595aab000c627
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