Instructions to use PraneshJs/PromptGuard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PraneshJs/PromptGuard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PraneshJs/PromptGuard")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PraneshJs/PromptGuard") model = AutoModelForSequenceClassification.from_pretrained("PraneshJs/PromptGuard") - Notebooks
- Google Colab
- Kaggle
File size: 487 Bytes
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"backend": "tokenizers",
"cls_token": "[CLS]",
"do_lower_case": true,
"is_local": false,
"local_files_only": false,
"mask_token": "[MASK]",
"max_length": 128,
"model_max_length": 1000000000000000019884624838656,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"stride": 0,
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": "[UNK]"
}
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