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
Training update: 4948 samples @ 2025-10-20 05:00:59
Browse files- training_metadata.json +3 -3
training_metadata.json
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{
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"trained_at":
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"trained_at_readable": "2025-10-20
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"samples":
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"final_loss": 0,
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"epochs": 3,
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"learning_rate": 5e-05
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{
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"trained_at": 1760936459.4350178,
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"trained_at_readable": "2025-10-20 05:00:59",
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"samples": 4948,
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"final_loss": 0,
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"epochs": 3,
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"learning_rate": 5e-05
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