Instructions to use CaffreyR/wgr_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CaffreyR/wgr_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CaffreyR/wgr_test")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CaffreyR/wgr_test") model = AutoModelForSequenceClassification.from_pretrained("CaffreyR/wgr_test") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6a12d18f614e7f7473da898253c9fe03f933005aa2c1eef43504c364ebaad48b
- Size of remote file:
- 439 MB
- SHA256:
- e3709f1860f0867c5102527fa48f2af8ddfb3dd0055e65ac165c11842800f7a1
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