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:
- 97b69f4e0824363d669a2ab0a64ff0820130d31123be7dea66fee46488c88ddb
- Size of remote file:
- 438 MB
- SHA256:
- 10a0fd8e09637e1e447b0782697bf17425e9626661de1d6fdde3d31821dabb12
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