Instructions to use JeremyNJ/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremyNJ/test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremyNJ/test")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremyNJ/test") model = AutoModelForSequenceClassification.from_pretrained("JeremyNJ/test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 83c80808fac99db781ec227be42485e42b05b1782aa1446507317e8329f52ad9
- Size of remote file:
- 438 MB
- SHA256:
- 9d966aa5800b15700a9512777a99b0342fdede2b7c4acd883f96f1d7aed9eaac
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