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
add reports/models.sha256
Browse files- reports/models.sha256 +10 -0
reports/models.sha256
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# SHA256 manifest for the tracked model checkpoints (gitignored *.pt, listed
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# here individually). Verify after a fresh clone with: sha256sum -c models.sha256
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# Re-generate with: sha256sum inspector/anomaly_model.pt inspector/side_channel_model.pt inspector/side_channel_model_real.pt planner/model_ddp_v3.pt planner/model_gpu_v3.pt planner/model_real_v3.pt planner/rl_agent.pt > models.sha256
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c9b8a0fc3a4f02c568e6c5cac1f2396a3a8033ccb8643e2745248310097a1eef inspector/anomaly_model.pt
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5d5bef07c82f80a29acbf41ce8535c494193a2216e4d083ddf0492294f30fff8 inspector/side_channel_model.pt
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e9f693090ea7b7f88a7dd66c5a00ef74fc9d863d8df88f1566731cc144c9d3e3 inspector/side_channel_model_real.pt
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2f2f9c8abcc813adeba4c7c2ad656c87aafedae26b912e9b22b95f127b6b51ec planner/model_ddp_v3.pt
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eaba1e60817df522b8adc654a084cd633e69c5c2aae878c185bcc3d147bf4d02 planner/model_gpu_v3.pt
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20978344fc06f72f74f11723ed1de21d7ec9df2abb60332cf9083347d7b0ef2a planner/model_real_v3.pt
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cffd9383b0cfa3ddd0ceec0926e09a6682a142ae4f13b79355bc8925422f6ecd planner/rl_agent.pt
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