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
- Xet hash:
- 8f4084fbadf90a8edbaf0651b9f59c838f104202305e6336304366d5e5408c08
- Size of remote file:
- 336 MB
- SHA256:
- 08a1be6db1475e8cbcf7ffbc2f80a3b64b82cc62438260bf5cfdb1d385407a76
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