Instructions to use digit82/test-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use digit82/test-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="digit82/test-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("digit82/test-model") model = AutoModelForSequenceClassification.from_pretrained("digit82/test-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f9452bee9c88e5ca65eb71397fe9217d5f3199ca2a2203678546d69177beda5
- Size of remote file:
- 443 MB
- SHA256:
- 2831ed5b9ee72d3a4fb25a267ee2316ba33b04493676caaf2f75874e2d648863
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