Sentence Similarity
Transformers
PyTorch
mpnet
feature-extraction
cybersecurity
sentence-embedding
text-embeddings-inference
Instructions to use basel/ATTACK-BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use basel/ATTACK-BERT with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("basel/ATTACK-BERT") model = AutoModel.from_pretrained("basel/ATTACK-BERT") - Inference
- Notebooks
- Google Colab
- Kaggle
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pipeline_tag: sentence-similarity
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ATT&CK BERT is a cybersecurity domain-specific language model based on [sentence-transformers](https://www.SBERT.net).
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ATT&CK BERT maps sentences representing attack actions to a semantically meaningful embedding vector.
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<!--- Describe your model here -->
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pipeline_tag: sentence-similarity
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tags:
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- cybersecurity
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- sentence-embedding
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ATT&CK BERT is a cybersecurity domain-specific language model based on [sentence-transformers](https://www.SBERT.net).
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ATT&CK BERT maps sentences representing attack actions to a semantically meaningful embedding vector.
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Embedding vectors of sentences with similar meanings have a high cosine similarity.
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<!--- Describe your model here -->
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