Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use CIRCL/vulnerability-severity-classification-roberta-base-expC 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-expC 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-expC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CIRCL/vulnerability-severity-classification-roberta-base-expC") model = AutoModelForSequenceClassification.from_pretrained("CIRCL/vulnerability-severity-classification-roberta-base-expC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bca35072defe7c94e4f7405656ba1a20dae208e227eb221057a880a38e0db0e8
- Size of remote file:
- 499 MB
- SHA256:
- ae81b0b9c2ca0fa8b3cbeac2199c684bbaa4f49a1ad0e7b437c81892e2f08cfe
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.