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-mid-cybersecurity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codechrl/bert-mid-cybersecurity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="codechrl/bert-mid-cybersecurity")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("codechrl/bert-mid-cybersecurity") model = AutoModelForMaskedLM.from_pretrained("codechrl/bert-mid-cybersecurity") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c7cd495c0369a07b234ea70bbb885d93c55dcefd566728c6c497ac4e94948fdf
- Size of remote file:
- 51.1 MB
- SHA256:
- 633158198375e49f46a2cf2df0ca3ecdbe7432bb5e3e33e270c8e3916c408227
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