Instructions to use Vrushali/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vrushali/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Vrushali/model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Vrushali/model") model = AutoModelForTokenClassification.from_pretrained("Vrushali/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c77c96d2ba0592a3ea2d4c66a9870fa8e3be312ffe1f408ab75508f24d04e55
- Size of remote file:
- 266 MB
- SHA256:
- 2734c4d36b33de5f0b489518f9328bcc2179156e481332cef78e7ee31b313424
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