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
improved model on short questions
Browse files- config.json +1 -1
- model.safetensors +1 -1
- tokenizer_config.json +1 -1
config.json
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"pad_token_id": 0,
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"problem_type": "single_label_classification",
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"tie_word_embeddings": true,
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"transformers_version": "5.
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"type_vocab_size": 2,
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"use_cache": false,
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"vocab_size": 30522
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"pad_token_id": 0,
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"problem_type": "single_label_classification",
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"tie_word_embeddings": true,
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"transformers_version": "5.12.0",
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"type_vocab_size": 2,
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"use_cache": false,
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"vocab_size": 30522
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 44692608
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version https://git-lfs.github.com/spec/v1
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oid sha256:ca394195e0265a40342626503e759295780eb99f213d954e211cbffe5d631a46
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size 44692608
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tokenizer_config.json
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"backend": "tokenizers",
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"cls_token": "[CLS]",
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"do_lower_case": true,
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"is_local":
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"local_files_only": false,
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"mask_token": "[MASK]",
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"max_length": 128,
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"backend": "tokenizers",
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"cls_token": "[CLS]",
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"do_lower_case": true,
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"is_local": false,
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"local_files_only": false,
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"mask_token": "[MASK]",
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"max_length": 128,
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