Instructions to use viv/AIKIA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use viv/AIKIA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="viv/AIKIA")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("viv/AIKIA") model = AutoModelForSequenceClassification.from_pretrained("viv/AIKIA") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1eb739aab4326b4d19d0eb3d2193790ec6c154110a5418db02a985244ee4f1bc
- Size of remote file:
- 452 MB
- SHA256:
- 672b80b62ae7e817815a31201bb064607a2d65b559a36f35805453c5ccf70775
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