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
Download training_metadata.json from codechrl/bert-micro-cybersecurity: direct link, hf CLI and curl.
- Browser
- Download file 302 Bytes
-
https://huggingface.co/codechrl/bert-micro-cybersecurity/resolve/4300aab7b2799ae6daae1b928e7eae29730c7d59/training_metadata.json
- Command line
-
hf download hf://codechrl/bert-micro-cybersecurity@4300aab7b2799ae6daae1b928e7eae29730c7d59/training_metadata.json
-
curl -L -o training_metadata.json https://huggingface.co/codechrl/bert-micro-cybersecurity/resolve/4300aab7b2799ae6daae1b928e7eae29730c7d59/training_metadata.json
302 Bytes
| { | |
| "trained_at": 1761021667.0032144, | |
| "trained_at_readable": "2025-10-21 04:41:07", | |
| "samples_this_session": 4634, | |
| "new_rows_this_session": 500, | |
| "trained_rows_total": 1616, | |
| "total_db_rows": 237718, | |
| "percentage": 0.679797070478466, | |
| "final_loss": 0, | |
| "epochs": 3, | |
| "learning_rate": 5e-05 | |
| } |