Instructions to use btan2/cappy-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use btan2/cappy-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="btan2/cappy-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("btan2/cappy-large") model = AutoModelForSequenceClassification.from_pretrained("btan2/cappy-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae905ea044beea9e0c3db9137d8f4ea83a1d0a67d49fe68b56b179cd3c11ae27
- Size of remote file:
- 1.42 GB
- SHA256:
- 471b5ac291c4e0827b3092e6eae42d40f04bbac3322187e10bd2f2bd5e117ba8
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