Instructions to use Vrushali/model_abv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vrushali/model_abv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Vrushali/model_abv")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Vrushali/model_abv") model = AutoModelForTokenClassification.from_pretrained("Vrushali/model_abv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c6210e0bf03821116d945d6a1f05b880d334ba19ba6d5bf33421779231389c61
- Size of remote file:
- 436 MB
- SHA256:
- 925d546ec615f6f28d6587267e56cd5496f390d5fbfc32c2befbb3d6da4106f9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.