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-mini-cybersecurity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codechrl/bert-mini-cybersecurity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="codechrl/bert-mini-cybersecurity")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("codechrl/bert-mini-cybersecurity") model = AutoModelForMaskedLM.from_pretrained("codechrl/bert-mini-cybersecurity") - Notebooks
- Google Colab
- Kaggle
| { | |
| "trained_at": 1775446427.337274, | |
| "trained_at_readable": "2026-04-06 10:33:47", | |
| "samples_this_session": 100, | |
| "new_rows_this_session": 100, | |
| "trained_rows_total": 0, | |
| "total_db_rows": 349936, | |
| "percentage": 0.0, | |
| "final_loss": 0, | |
| "epochs": 3, | |
| "learning_rate": 5e-05, | |
| "batch_size": 16, | |
| "stride": 32, | |
| "max_length": 512 | |
| } |