Text Classification
Transformers
Safetensors
Chinese
bert
vulnerability
severity
cybersecurity
cnvd
text-embeddings-inference
Instructions to use CIRCL/vulnerability-severity-classification-chinese-macbert-base-test 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-test 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-test")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CIRCL/vulnerability-severity-classification-chinese-macbert-base-test") model = AutoModelForSequenceClassification.from_pretrained("CIRCL/vulnerability-severity-classification-chinese-macbert-base-test") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8ec6bce928528264c4479dd86ab8326a4f821eefe2803ec7e4d89911d6da643d
- Size of remote file:
- 5.27 kB
- SHA256:
- 841221ed0c084667e4dde4275f8b6c281a157b9d51a0da45e3fe9ffb046c2ac2
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