Feature Extraction
Transformers
Safetensors
English
mpnet
cybersecurity
classification
fine-tuned
text-embeddings-inference
Instructions to use selfconstruct3d/AttackGroup-MPNET with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selfconstruct3d/AttackGroup-MPNET with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="selfconstruct3d/AttackGroup-MPNET")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("selfconstruct3d/AttackGroup-MPNET") model = AutoModel.from_pretrained("selfconstruct3d/AttackGroup-MPNET") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 29406a1f33e6d194f0fcbd3a67d84abfbc2833db2b1a6d112565754e29b8bbb0
- Size of remote file:
- 439 MB
- SHA256:
- fef2486bdf5ac34fdd0deff48147a27ab96fc409f9be6725c14ef521326152cd
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