Instructions to use aloxatel/KS8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aloxatel/KS8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aloxatel/KS8")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aloxatel/KS8") model = AutoModelForSequenceClassification.from_pretrained("aloxatel/KS8") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f703a7eb91d7ea2e19d6abe8727343081e045fabc55cca9353c48312739ae0f4
- Size of remote file:
- 1.42 GB
- SHA256:
- 3fd0c8faf454b13c3261a4d088aee4980789e63aed0ee3a3949116d7877bc687
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