Instructions to use jndvs/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jndvs/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jndvs/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jndvs/results") model = AutoModelForSequenceClassification.from_pretrained("jndvs/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8e462d82d43080cf5092139c689947d8d9003a0018a2b517d523a9045f207baf
- Size of remote file:
- 5.78 kB
- SHA256:
- f982f46252fa6b85bf5c8d96aa29a81aa57d5bcd4939642b2a4dd38225de9d68
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