Fill-Mask
Transformers
Safetensors
PyTorch
English
Indonesian
bert
text-classification
token-classification
cybersecurity
named-entity-recognition
tensorflow
masked-language-modeling
Instructions to use codechrl/bert-mini-cybersecurity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codechrl/bert-mini-cybersecurity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="codechrl/bert-mini-cybersecurity")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("codechrl/bert-mini-cybersecurity") model = AutoModelForMaskedLM.from_pretrained("codechrl/bert-mini-cybersecurity") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c024d669e70bda74925232b7d5ce3631d76047c009ee8837be8c04686b8260be
- Size of remote file:
- 19.3 MB
- SHA256:
- 912ab7a7e426b316b960b61f3dfca4590dd4638b79e684ea7de8868eaee97d82
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