Text Classification
Transformers
Safetensors
Chinese
bert
vulnerability
severity
cybersecurity
cnvd
text-embeddings-inference
Instructions to use CIRCL/vulnerability-severity-classification-chinese-macbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIRCL/vulnerability-severity-classification-chinese-macbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CIRCL/vulnerability-severity-classification-chinese-macbert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CIRCL/vulnerability-severity-classification-chinese-macbert-base") model = AutoModelForSequenceClassification.from_pretrained("CIRCL/vulnerability-severity-classification-chinese-macbert-base", 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 | |
| 2026-08-18T07:43:00,VulnTrain,b9dc86ff-1b4b-4d0a-86e2-8f31b6aafdd5,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,3848.338559512049,0.08975977537364227,2.332429280469112e-05,70.00062838110752,656.1602428069918,70.0,0.07482258372379585,0.703132053060699,0.0747645083438243,0.8527191451283193,0.0,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,1.2381609794217245,74.44139098723626,2.237900494920552,44.93622604513206,N,1.0,0.0 | |