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 training_args.bin from Sunbird/sunflower_language_classification_v2: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/Sunbird/sunflower_language_classification_v2/resolve/main/training_args.bin
- Command line
-
hf download hf://Sunbird/sunflower_language_classification_v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Sunbird/sunflower_language_classification_v2/resolve/main/training_args.bin
5.5 kB
- Xet hash:
- 2c9200b9fa84fa06c9b7eb436360fd84a73c51f556748784ec62fff1a7bdcef2
- Size of remote file:
- 5.5 kB
- SHA256:
- 26c99905d2adfe9b18ad2ddb0f65516142c89eaeeb6e70f7a8f89c22cb7058e8
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