Fill-Mask
Transformers
PyTorch
Safetensors
English
bert
Cybersecurity
Cyber Security
Information Security
Computer Science
Cyber Threats
Vulnerabilities
Vulnerability
Malware
Attacks
Instructions to use markusbayer/CySecBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use markusbayer/CySecBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="markusbayer/CySecBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("markusbayer/CySecBERT") model = AutoModelForMaskedLM.from_pretrained("markusbayer/CySecBERT", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from markusbayer/CySecBERT: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/markusbayer/CySecBERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://markusbayer/CySecBERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/markusbayer/CySecBERT/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 9d820ac65c07c1449a804f5d264878d586630cb908e23c254460304dd5abf10e
- Size of remote file:
- 438 MB
- SHA256:
- bcdf4b53d8b19f476a88e08a8f6393ec8c1255f07c77007d84ec4fbdbca3ea5a
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