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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("codechrl/bert-micro-cybersecurity") model = AutoModelForMaskedLM.from_pretrained("codechrl/bert-micro-cybersecurity", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 362 Bytes
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"trained_at": 1761743357.5986657,
"trained_at_readable": "2025-10-29 13:09:17",
"samples_this_session": 2255,
"new_rows_this_session": 683,
"trained_rows_total": 131944,
"total_db_rows": 240595,
"percentage": 54.840707412872256,
"final_loss": 0,
"epochs": 3,
"learning_rate": 5e-05,
"batch_size": 16,
"stride": 32,
"max_length": 512
} |