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: 358 Bytes
17995d0 4cd0124 63ab79d 4cd0124 63ab79d 4cd0124 17995d0 ae1a849 17995d0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"trained_at": 1773199843.8985076,
"trained_at_readable": "2026-03-11 03:30:43",
"samples_this_session": 894,
"new_rows_this_session": 2,
"trained_rows_total": 163264,
"total_db_rows": 336594,
"percentage": 48.50472676280623,
"final_loss": 0,
"epochs": 3,
"learning_rate": 5e-05,
"batch_size": 16,
"stride": 32,
"max_length": 512
} |