Instructions to use profoz/deploy-mlops-demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use profoz/deploy-mlops-demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="profoz/deploy-mlops-demo")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("profoz/deploy-mlops-demo") model = AutoModelForSequenceClassification.from_pretrained("profoz/deploy-mlops-demo") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a97517524a516dcd8e1ab73d68773e7618ac7262f3d381acbd27bb3a16d847cb
- Size of remote file:
- 268 MB
- SHA256:
- 2b3af466d190147677bbf8d6e99b9829fef9bafa5d6540a204274ffc9ad78917
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