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
Training update: 10/67,618 rows (0.01%) @ 2025-10-20 05:16:42
Browse files- training_metadata.json +6 -3
training_metadata.json
CHANGED
|
@@ -1,7 +1,10 @@
|
|
| 1 |
{
|
| 2 |
-
"trained_at":
|
| 3 |
-
"trained_at_readable": "2025-10-20 05:
|
| 4 |
-
"samples":
|
|
|
|
|
|
|
|
|
|
| 5 |
"final_loss": 0,
|
| 6 |
"epochs": 3,
|
| 7 |
"learning_rate": 5e-05
|
|
|
|
| 1 |
{
|
| 2 |
+
"trained_at": 1760937402.585093,
|
| 3 |
+
"trained_at_readable": "2025-10-20 05:16:42",
|
| 4 |
+
"samples": 10,
|
| 5 |
+
"trained_rows": 10,
|
| 6 |
+
"total_db_rows": 67618,
|
| 7 |
+
"percentage": 0.014788961519122127,
|
| 8 |
"final_loss": 0,
|
| 9 |
"epochs": 3,
|
| 10 |
"learning_rate": 5e-05
|