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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06292e67affdc5df78503866680e32a2b0e8fbf4d213d9d1bf7e4792f7440eec
- Size of remote file:
- 1.42 GB
- SHA256:
- 12c75d1559962148fbaeec6afebdd4657cdf2777fa12b36763c12ee5044ac4cd
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