Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use CIRCL/vulnerability-attack-technique-classification-pilot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIRCL/vulnerability-attack-technique-classification-pilot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CIRCL/vulnerability-attack-technique-classification-pilot")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CIRCL/vulnerability-attack-technique-classification-pilot") model = AutoModelForSequenceClassification.from_pretrained("CIRCL/vulnerability-attack-technique-classification-pilot", 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 | |
| 2026-07-14T05:43:20,VulnTrain,ce58936d-aceb-404c-ac40-fbdeb5970871,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,287.4541791751981,0.005600634657486989,1.948357360312895e-05,70.00013690517649,516.5056235887321,70.0,0.005386460057389911,0.04243354450238712,0.0053861044955129415,0.05320610905528997,0.0,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-106-generic-x86_64-with-glibc2.39,3.12.3,3.2.8,224,Intel(R) Xeon(R) Platinum 8480+,2,2 x NVIDIA H100 NVL,6.1327,49.6098,2015.336296081543,machine,0.9485915492957746,54.026408450704224,1.730281690140845,35.15862096867091,N,1.0,0.0 | |