Instructions to use Sunbird/sunflower_language_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sunbird/sunflower_language_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sunbird/sunflower_language_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sunbird/sunflower_language_classification") model = AutoModelForSequenceClassification.from_pretrained("Sunbird/sunflower_language_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 915d29ca8d034833489b2c6b751414e210bc22d93de628e9b03ae81e0b583407
- Size of remote file:
- 5.33 kB
- SHA256:
- a4e3aa797123ac167307658f133886f118137de20d3071fe677d42112b37086d
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