Instructions to use nhanv/VBHC-classify with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nhanv/VBHC-classify with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nhanv/VBHC-classify")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nhanv/VBHC-classify") model = AutoModelForSequenceClassification.from_pretrained("nhanv/VBHC-classify") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 40ea03ce5be652ec4ef9a102341e9ce0cc76fc05b119830b475878297f5cbc35
- Size of remote file:
- 535 MB
- SHA256:
- 01faa9e19d8b7dc11a3ba7d17425ee3646e7d0c57a88b4624a13ad906d2f1380
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