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:
- d8f0c40ae300cf8491174fbb98627b51edb13b84c9297f95bef27ef958e755e7
- Size of remote file:
- 261 MB
- SHA256:
- 8a9f9b2f153ac9ff230aca4548fa3286be9d2f9ea4eb7e9169665b1a8e983f44
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.