Instructions to use Sunbird/sunflower_language_classification_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sunbird/sunflower_language_classification_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sunbird/sunflower_language_classification_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sunbird/sunflower_language_classification_v2") model = AutoModelForSequenceClassification.from_pretrained("Sunbird/sunflower_language_classification_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from Sunbird/sunflower_language_classification_v2: direct link, hf CLI and curl.
- Browser
- Download file 548 kB
-
https://huggingface.co/Sunbird/sunflower_language_classification_v2/resolve/main/spiece.model
- Command line
-
hf download hf://Sunbird/sunflower_language_classification_v2/spiece.model
-
curl -L -o spiece.model https://huggingface.co/Sunbird/sunflower_language_classification_v2/resolve/main/spiece.model
548 kB
- Xet hash:
- 1e71575891ff21953e54d9fa0f9a211687d829ab8dca69ee7400b74a4be03b50
- Size of remote file:
- 548 kB
- SHA256:
- 10fbdae1388f6e2d523c1fc6ccf96fbd7ace5468c6a0e43bfff7e45891f62743
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