Instructions to use KiViDrag/model3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KiViDrag/model3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KiViDrag/model3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KiViDrag/model3") model = AutoModelForSequenceClassification.from_pretrained("KiViDrag/model3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7958e16922268e26db0680b626dc769f8d80420d327ebaef93cc017f720fcd82
- Size of remote file:
- 29.3 MB
- SHA256:
- 4020e56e86d6701e9994515324d258aca377aa32175ebb7980ecb1428ce65ae5
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