Text Classification
Transformers
ONNX
English
deberta-v2
ai-safety
prompt-injection-defender
jailbreak-defender
adversarial-input-detection
text-embeddings-inference
Instructions to use Yeger/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yeger/test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Yeger/test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Yeger/test") model = AutoModelForSequenceClassification.from_pretrained("Yeger/test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download onnx/model_quantized.onnx from Yeger/test: direct link, hf CLI and curl.
- Browser
- Download file 568 MB
-
https://huggingface.co/Yeger/test/resolve/main/onnx/model_quantized.onnx
- Command line
-
hf download hf://Yeger/test/onnx/model_quantized.onnx
-
curl -L -o model_quantized.onnx https://huggingface.co/Yeger/test/resolve/main/onnx/model_quantized.onnx
568 MB
- Xet hash:
- 8627829de7dd637b8338fdf55fbcdfc8625f2a58353da85fc96c1ffb33d437ae
- Size of remote file:
- 568 MB
- SHA256:
- a70600837172031b9b5ec35c7b2e6f15ed4e91b9a5c6137d43df679fa09a4be5
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