Text Classification
Transformers
Safetensors
Russian
roberta
vulnerability
severity
cybersecurity
fstec
Generated from Trainer
text-embeddings-inference
Instructions to use CIRCL/vulnerability-severity-classification-russian-ruRoberta-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIRCL/vulnerability-severity-classification-russian-ruRoberta-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CIRCL/vulnerability-severity-classification-russian-ruRoberta-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CIRCL/vulnerability-severity-classification-russian-ruRoberta-large") model = AutoModelForSequenceClassification.from_pretrained("CIRCL/vulnerability-severity-classification-russian-ruRoberta-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c29787af06a65d5267546ff152032de0643c5b3bff65593330874eec58c8d0b
- Size of remote file:
- 5.27 kB
- SHA256:
- c2da8bcf496a32af15208b36b8293f6626aaf4cd3f1122beaa520bc59a105491
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.