Instructions to use jeromeku/pytorch2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jeromeku/pytorch2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jeromeku/pytorch2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jeromeku/pytorch2") model = AutoModelForSequenceClassification.from_pretrained("jeromeku/pytorch2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4aa5e5a6d81bb605e2ce72c682ecde1cd6c7fcddd66ea6f294a194115132944
- Size of remote file:
- 3.58 kB
- SHA256:
- 1bc1bf8ce5e9e4ebcfe3491a559df309455a1c83dc63a6f37f104d1327dd3c70
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