Fill-Mask
Transformers
PyTorch
Safetensors
English
bert
exbert
security
cybersecurity
cyber security
threat hunting
threat intelligence
Instructions to use jackaduma/SecBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jackaduma/SecBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jackaduma/SecBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jackaduma/SecBERT") model = AutoModelForMaskedLM.from_pretrained("jackaduma/SecBERT") - Inference
- Notebooks
- Google Colab
- Kaggle
SecBERT
This is the pretrained model presented in SecBERT: A Pretrained Language Model for Cyber Security Text, which is a BERT model trained on cyber security text.
The training corpus was papers taken from
- APTnotes
- Stucco-Data: Cyber security data sources
- CASIE: Extracting Cybersecurity Event Information from Text
- SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using Natural Language Processing (SecureNLP).
SecBERT has its own wordpiece vocabulary (secvocab) that's built to best match the training corpus.
We trained SecBERT and SecRoBERTa versions.
Available models include:
Fill Mask
We proposed to build language model which work on cyber security text, as result, it can improve downstream tasks (NER, Text Classification, Semantic Understand, Q&A) in Cyber Security Domain.
First, as below shows Fill-Mask pipeline in Google Bert, AllenAI SciBert and our SecBERT .
The original repo can be found here.
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