Sentence Similarity
Transformers
Safetensors
English
roberta
feature-extraction
security
vulnerability
mitre-attack
cve
bi-encoder
text-embeddings-inference
Instructions to use CIRCL/vulnerability-attack-technique-biencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIRCL/vulnerability-attack-technique-biencoder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("CIRCL/vulnerability-attack-technique-biencoder") model = AutoModel.from_pretrained("CIRCL/vulnerability-attack-technique-biencoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| timestamp,project_name,run_id,experiment_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,water_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,cpu_utilization_percent,gpu_utilization_percent,ram_utilization_percent,ram_used_gb,on_cloud,pue,wue | |
| 2026-08-12T12:45:11,VulnTrain,533559ff-f13e-4930-8f5e-afb656bc6ad3,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,481.90460335463285,0.012102830705021725,2.5114577907684503e-05,70.00025546306983,695.0731979000326,70.0,0.009369809123523486,0.09624448393998364,0.009362771099795483,0.11497706416330263,0.0,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-106-generic-x86_64-with-glibc2.39,3.12.3,3.3.0,224,Intel(R) Xeon(R) Platinum 8480+,2,2 x NVIDIA H100 NVL,6.1327,49.6098,2015.336296081543,machine,1.1429166666666668,83.196875,2.00625,40.65581835905711,N,1.0,0.0 | |