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-medium-cybersecurity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codechrl/bert-medium-cybersecurity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="codechrl/bert-medium-cybersecurity")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("codechrl/bert-medium-cybersecurity") model = AutoModelForMaskedLM.from_pretrained("codechrl/bert-medium-cybersecurity") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1ae9d6dc6fe0bb4c02d2cee4baafca2abc3454fb7352e823f4c881b226ae29be
- Size of remote file:
- 166 MB
- SHA256:
- 61b5b546f3713ec8614600fa736f8c27ac2de1c1f479f56d87f77ad20e2e1308
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