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:
- c23f6fd2958f8604b352eff994c8cda16cf9989c0297f6a057af376e60ff64dc
- Size of remote file:
- 261 MB
- SHA256:
- 63ee5a0142067161ced524179c161c5026f47b53a34a946a5ad1a907fab35011
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