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
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- mpnet
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- classification
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- fine-tuned
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license: creativeml-openrail-m
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language:
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- en
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base_model:
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## Model Card Contact
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- https://www.linkedin.com/in/dzenan-hamzic/
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- mpnet
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- classification
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- fine-tuned
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language:
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- en
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base_model:
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## Model Card Contact
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- https://www.linkedin.com/in/dzenan-hamzic/
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This model is licensed for non-commercial use only (CC BY-NC 4.0).
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For commercial inquiries, please contact dzenan.hamzic@ait.ac.at.
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