Text Classification
Transformers
ONNX
Safetensors
roberta
code
cryptography
post-quantum
static-analysis
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 gnn/model_gpu_v3.pt from KRISHNAPURI/q-trust-codebert: direct link, hf CLI and curl.
- Browser
- Download file 967 kB
-
https://huggingface.co/KRISHNAPURI/q-trust-codebert/resolve/main/gnn/model_gpu_v3.pt
- Command line
-
hf download hf://KRISHNAPURI/q-trust-codebert/gnn/model_gpu_v3.pt
-
curl -L -o model_gpu_v3.pt https://huggingface.co/KRISHNAPURI/q-trust-codebert/resolve/main/gnn/model_gpu_v3.pt
967 kB
- Xet hash:
- eda9bbb317dbd5f7f0ddfc4919b44dc812ef405360dd25155b8a8b360d934a53
- Size of remote file:
- 967 kB
- SHA256:
- eaba1e60817df522b8adc654a084cd633e69c5c2aae878c185bcc3d147bf4d02
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