Text Classification
Transformers
Safetensors
Chinese
bert
Generated from Trainer
classification
nlp
chinese
vulnerability
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") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add pipeline tag, language, paper, project, code, and usage
#1
by nielsr HF Staff - opened
This PR significantly enhances the model card for vulnerability-severity-classification-chinese-macbert-base by:
- Adding
pipeline_tag: text-classificationandlanguage: zhto the metadata for improved discoverability and accurate filtering on the Hugging Face Hub. - Including more descriptive
tagssuch astext-classification,classification,nlp,chinese, andvulnerability. - Updating the main title to
VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classificationto align with the associated research paper. - Adding a clear description of the model based on the paper's abstract.
- Providing direct links to the paper (VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification), the project page (
https://vulnerability.circl.lu), and the associated GitHub repository (https://github.com/vulnerability-lookup/ML-Gateway). - Adding a practical Python sample usage snippet using the
transformerslibrary, including an example with Chinese text. - Removing the auto-generated comment as the model card has now been manually improved.
These additions provide users with richer context and make the model more accessible and understandable.
thanks !
cedricbonhomme changed pull request status to merged