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:
- 1fccac1775966ed597b9eeb522c0f2beb3cca05ddc53eb30b8946b5f79a76f08
- Size of remote file:
- 438 MB
- SHA256:
- 3c6f94241f4c4fa6e77dd6e9a3591839441c9e5f4229817f9dd2424cd8911bf0
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