Instructions to use Nav772/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nav772/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Nav772/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Nav772/results") model = AutoModelForSequenceClassification.from_pretrained("Nav772/results") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f91f3b4c81c6cc73917e1449c0c5b73cc0a0a834786735ae9c09f3926a4c43c
- Size of remote file:
- 18 kB
- SHA256:
- 97be9e81427af2effb1eff2f48b10e810c8a9023fcc7625a89ce2f7746834591
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