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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8c406e2006c2b04a9863126e1b6e2d73aef37fd9b4ef8fd5d2e94f5cf675ec63
- Size of remote file:
- 261 MB
- SHA256:
- fbe35d48b0d713cac84817ee8f7433c0ae31c3ec91275c33ab0811bb482ca993
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