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
| { | |
| "trained_at": 1760953333.2519712, | |
| "trained_at_readable": "2025-10-20 09:42:13", | |
| "samples_this_session": 1, | |
| "new_rows_this_session": 1, | |
| "trained_rows_total": 1599, | |
| "total_db_rows": 237655, | |
| "percentage": 0.6728240516715407, | |
| "final_loss": 0, | |
| "epochs": 3, | |
| "learning_rate": 5e-05 | |
| } |