Instructions to use dcarpintero/pangolin-guard-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dcarpintero/pangolin-guard-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dcarpintero/pangolin-guard-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dcarpintero/pangolin-guard-large") model = AutoModelForSequenceClassification.from_pretrained("dcarpintero/pangolin-guard-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 73d432d3c752e8ec17e18fbb23c2bb496dbb5d36cacc44ecacc4d6fb1d5ac3b0
- Size of remote file:
- 5.37 kB
- SHA256:
- ac32a7df877d47dcba3147a863c03b4dada3a5034c3588193c1f61d98a8bbdff
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