Token Classification
Transformers
PyTorch
TensorFlow
Rust
Safetensors
OpenVINO
English
distilbert
Eval Results (legacy)
Instructions to use wbq/model-api-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wbq/model-api-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="wbq/model-api-test")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wbq/model-api-test") model = AutoModelForTokenClassification.from_pretrained("wbq/model-api-test") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 564400c6a2f6391b7cba304ed3ed968a8c1fae08554a7ae5d9732fdc9033af27
- Size of remote file:
- 261 MB
- SHA256:
- f198de8ef6e40aeccd6eaa86e34dde73c3bb4bf0e54003cd182a18c29a1811db
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