Instructions to use liamvbetts/SNHv1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liamvbetts/SNHv1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="liamvbetts/SNHv1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("liamvbetts/SNHv1") model = AutoModelForSequenceClassification.from_pretrained("liamvbetts/SNHv1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ded9e8ae2856ccf81f7c087bd5abd24b08037313a071f3d701d9fbe613b74b4f
- Size of remote file:
- 433 MB
- SHA256:
- 3d7e5f2747d3c1218781b4728a3ac37d760895296343906a5b22d2871c64d75f
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