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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 20eecff3a8ed0da983544cdafe1f50990adab960d9a688f2f75406d1af0b5458
- Size of remote file:
- 62.6 MB
- SHA256:
- 2f2e5888ff7741e36c1f60cf0894267db00e3f69abbc7f540bc45918531dd55e
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