Instructions to use yangwooko/a_different_name with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yangwooko/a_different_name with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yangwooko/a_different_name")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yangwooko/a_different_name") model = AutoModelForSequenceClassification.from_pretrained("yangwooko/a_different_name", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 39def2ca3ec2567846e3e1a7e1904633fdc61b451a90e72b95b8456a68465766
- Size of remote file:
- 3.58 kB
- SHA256:
- 840a6b0d8ccec997a709344150c0d5725a55c7cabae3ce153471bcbc82c2e8a2
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