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
Kun Ma commited on
Commit ·
feb9943
1
Parent(s): 21e0aa6
update README
Browse files
README.md
CHANGED
|
@@ -3,8 +3,11 @@ language: en
|
|
| 3 |
thumbnail: https://github.com/jackaduma
|
| 4 |
tags:
|
| 5 |
- exbert
|
| 6 |
-
- cyber security
|
| 7 |
- security
|
|
|
|
|
|
|
|
|
|
|
|
|
| 8 |
license: apache-2.0
|
| 9 |
datasets:
|
| 10 |
- APTnotes
|
|
|
|
| 3 |
thumbnail: https://github.com/jackaduma
|
| 4 |
tags:
|
| 5 |
- exbert
|
|
|
|
| 6 |
- security
|
| 7 |
+
- cybersecurity
|
| 8 |
+
- cyber security
|
| 9 |
+
- threat hunting
|
| 10 |
+
- threat intelligence
|
| 11 |
license: apache-2.0
|
| 12 |
datasets:
|
| 13 |
- APTnotes
|