Instructions to use KiViDrag/model2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KiViDrag/model2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KiViDrag/model2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KiViDrag/model2") model = AutoModelForSequenceClassification.from_pretrained("KiViDrag/model2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b7fa4609459c195a58167ebd7e47bed5d8210fdde5d4ef34df8fc2100e752402
- Size of remote file:
- 29.3 MB
- SHA256:
- 96a01146f1bd02259d01c7efbd3f65be8a898f68b796b26e4211af35e1a1329d
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