Text Classification
Transformers
Safetensors
modernbert
ai-safety
safeguards
guardrails
text-embeddings-inference
Instructions to use dcarpintero/pangolin-guard-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dcarpintero/pangolin-guard-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dcarpintero/pangolin-guard-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dcarpintero/pangolin-guard-base") model = AutoModelForSequenceClassification.from_pretrained("dcarpintero/pangolin-guard-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dcarpintero/pangolin-guard-base: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/dcarpintero/pangolin-guard-base/resolve/697a49173e7e27664272943a7189b8f32975e283/training_args.bin
- Command line
-
hf download hf://dcarpintero/pangolin-guard-base@697a49173e7e27664272943a7189b8f32975e283/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dcarpintero/pangolin-guard-base/resolve/697a49173e7e27664272943a7189b8f32975e283/training_args.bin
5.37 kB
- Xet hash:
- b184a2cb7522ac12341283a0ac3e4af3689754dcedf605b7a81790d8dda56d34
- Size of remote file:
- 5.37 kB
- SHA256:
- 7728eb88b2c06723a8f260f74ea06bda3cfa88404faa10ba69440eb73d3acd18
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