Instructions to use WilliamWen/extract_features with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WilliamWen/extract_features with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="WilliamWen/extract_features")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("WilliamWen/extract_features") model = AutoModelForTokenClassification.from_pretrained("WilliamWen/extract_features", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a47ece1959f2be29d2800b893efc4cbd9c6c2925a12472f2eebad923e6f1d3c
- Size of remote file:
- 431 MB
- SHA256:
- b8e15ffbede5bc146c06a4e731da573ab2026f3ab7c13b43cef13f6f81b739d6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.