Instructions to use CaffreyR/test1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CaffreyR/test1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CaffreyR/test1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CaffreyR/test1") model = AutoModelForSequenceClassification.from_pretrained("CaffreyR/test1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8fb9ea0aa8bc40b80430c733316bc138da39a8a26c2158e9629909b111db3e7d
- Size of remote file:
- 439 MB
- SHA256:
- c789ff8fac0a387cbd5803daba2e4823c7e804bba82d9e97ede12c5ea0049fd5
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