Text Classification
Transformers
PyTorch
ONNX
Safetensors
deberta-v2
llm-guard
security
text-embeddings-inference
Instructions to use TangoBeeAkto/deberta-v3-base-zeroshot-v1.1-all-33 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TangoBeeAkto/deberta-v3-base-zeroshot-v1.1-all-33 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TangoBeeAkto/deberta-v3-base-zeroshot-v1.1-all-33")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TangoBeeAkto/deberta-v3-base-zeroshot-v1.1-all-33") model = AutoModelForSequenceClassification.from_pretrained("TangoBeeAkto/deberta-v3-base-zeroshot-v1.1-all-33", device_map="auto") - Notebooks
- Google Colab
- Kaggle
deberta-v3-base-zeroshot-v1.1-all-33
This model is used by LLM Guard for zero-shot classification.
Base Model: MoritzLaurer/deberta-v3-base-zeroshot-v1.1-all-33
Repository: https://github.com/akto-api-security/llm-guard
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