Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
classification
nlp
vulnerability
text-embeddings-inference
Instructions to use CIRCL/vulnerability-severity-classification-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIRCL/vulnerability-severity-classification-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CIRCL/vulnerability-severity-classification-roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CIRCL/vulnerability-severity-classification-roberta-base") model = AutoModelForSequenceClassification.from_pretrained("CIRCL/vulnerability-severity-classification-roberta-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2f0df0e08ec5ca0bdb4fdc17fa17e231e3c78dbde5f7fd16a6ae306405818cf
- Size of remote file:
- 5.27 kB
- SHA256:
- a09f00c329f360c419ac28dd62f3535f567379e4e794921925634379badaec6f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.