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 crypto_codebert/model.safetensors from KRISHNAPURI/q-trust-codebert: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/KRISHNAPURI/q-trust-codebert/resolve/main/crypto_codebert/model.safetensors
- Command line
-
hf download hf://KRISHNAPURI/q-trust-codebert/crypto_codebert/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/KRISHNAPURI/q-trust-codebert/resolve/main/crypto_codebert/model.safetensors
334 MB
- Xet hash:
- 9926cad13a10b97a8c8bb5f6a1eb45c1f504deb493f9a42797987b2b1e0c7e77
- Size of remote file:
- 334 MB
- SHA256:
- f025d70e9aa80faa89dfdf9ee03fa71ef5dee4352cba1731fc099e5133ecd77a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.