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:
- 72aba11ba461cef621365909d8b28ef0c970e477961cbc95383d7299757f2358
- Size of remote file:
- 439 MB
- SHA256:
- 85ec37f72cab5e5818fcbee2802a0cf1785a5ca14333ecd539c07387943d907d
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