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-micro-cybersecurity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codechrl/bert-micro-cybersecurity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="codechrl/bert-micro-cybersecurity")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("codechrl/bert-micro-cybersecurity") model = AutoModelForMaskedLM.from_pretrained("codechrl/bert-micro-cybersecurity") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 028fcb2fe7746bfe863fcd33a32f0d651c04c877fbad8c227103de7cf8cb39e8
- Size of remote file:
- 5.84 kB
- SHA256:
- 9c3f99d1b2b59eb11e0e6b1b29c895699e0cfe97c3c0f3ec2281a936854293a0
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