Instructions to use osanseviero/khipu_example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use osanseviero/khipu_example with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="osanseviero/khipu_example")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("osanseviero/khipu_example") model = AutoModelForSequenceClassification.from_pretrained("osanseviero/khipu_example") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 33a339c4eec70f3fe5a8bf306819f46e562e7786e3ad0d30f503feb561456c4a
- Size of remote file:
- 433 MB
- SHA256:
- d12833e7cee618acc729df7792bf37d323408e88e78cdbf0b2b6e0704538992c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.