Text Classification
Transformers
ONNX
Safetensors
roberta
code
cryptography
post-quantum
static-analysis
legacy
text-embeddings-inference
Instructions to use KRISHNAPURI/q-trust-codebert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KRISHNAPURI/q-trust-codebert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KRISHNAPURI/q-trust-codebert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KRISHNAPURI/q-trust-codebert") model = AutoModelForSequenceClassification.from_pretrained("KRISHNAPURI/q-trust-codebert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download onnx/model_quantized.onnx from KRISHNAPURI/q-trust-codebert: direct link, hf CLI and curl.
- Browser
- Download file 83.9 MB
-
https://huggingface.co/KRISHNAPURI/q-trust-codebert/resolve/main/onnx/model_quantized.onnx
- Command line
-
hf download hf://KRISHNAPURI/q-trust-codebert/onnx/model_quantized.onnx
-
curl -L -o model_quantized.onnx https://huggingface.co/KRISHNAPURI/q-trust-codebert/resolve/main/onnx/model_quantized.onnx
83.9 MB
- Xet hash:
- 422b50d9ff1bafa1a155d6e807e5992a4af4fbbba3d3c74de9e914f2da29b2b0
- Size of remote file:
- 83.9 MB
- SHA256:
- c507dddc8cab6ff7c64f388211ea90a65ac5a2d5b64850ee5a47c4bb0066d770
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