Instructions to use sgugger/debug-example2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgugger/debug-example2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sgugger/debug-example2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sgugger/debug-example2") model = AutoModelForSequenceClassification.from_pretrained("sgugger/debug-example2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 43a74232a8844ff944fd2c9825760504bc518b828d721129ad21527834c8392d
- Size of remote file:
- 268 MB
- SHA256:
- 82ac9a6db0a6d483463c689d991c5e993c089a626563e9f7c4a94a8e3dae88d0
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