Training update: 7,394/238,453 rows (3.10%) | +2 new @ 2025-10-23 03:13:01

README.md

@@ -24,7 +24,7 @@ pipeline_tag: fill-mask
 - Model type: fine-tuned lightweight BERT variant  
 - Languages: English & Indonesia
 - Finetuned from: `boltuix/bert-micro`
-- Status: **Early version** — trained on **2.89%** of planned data.
+- Status: **Early version** — trained on **3.10%** of planned data.
 **Model sources**  
 - Base model: [boltuix/bert-micro](https://huggingface.co/boltuix/bert-micro)
 - Data: Cybersecurity Data
@@ -40,7 +40,7 @@ You can use this model to classify cybersecurity-related text — for example, w
 - Not optimized for languages other than English and Indonesian.  
 - Not tested for non-cybersecurity domains or out-of-distribution data.
 ## 3. Bias, Risks, and Limitations 
-Because the model is based on a small subset (2.89%) of planned data, performance is preliminary and may degrade on unseen or specialized domains (industrial control, IoT logs, foreign language).
+Because the model is based on a small subset (3.10%) of planned data, performance is preliminary and may degrade on unseen or specialized domains (industrial control, IoT logs, foreign language).
 - Inherits any biases present in the base model (`boltuix/bert-micro`) and in the fine-tuning data — e.g., over-representation of certain threat types, vendor or tooling-specific vocabulary.
 - Should not be used as sole authority for incident decisions; only as an aid to human analysts.
 ## 4. How to Get Started with the Model  
@@ -75,11 +75,11 @@ Since cybersecurity data often contains lengthy alert descriptions and execution
 
 ### Training Data
 - **Total database rows**: 238,453
-- **Rows processed (cumulative)**: 6,893 (2.89%)
-- **Rows in this session**: 501
-- **Training samples (after chunking)**: 501
-- **Training date**: 2025-10-23 03:09:37
+- **Rows processed (cumulative)**: 7,394 (3.10%)
+- **Rows in this session**: 2
+- **Training samples (after chunking)**: 36
+- **Training date**: 2025-10-23 03:13:01
 
 ### Post-Training Metrics
-- **Final training loss**: 
-- **Rows→Samples ratio**: 1.00x (average chunks per row)
+- **Final training loss**: 0.0000
+- **Rows→Samples ratio**: 18.00x (average chunks per row)

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:e30526b15f07930b5f6c28af774791544eb6f2c043df5e0d50fa2981be2b466e
 size 17671560

training_args.bin

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+oid sha256:20e41b42ff4730cb2c54ef3d0267fccb02345579a722af22428d0e89a570f703
 size 5905

training_metadata.json

@@ -1,11 +1,11 @@
 {
-  "trained_at": 1761188977.395925,
-  "trained_at_readable": "2025-10-23 03:09:37",
-  "samples_this_session": 501,
-  "new_rows_this_session": 501,
-  "trained_rows_total": 6893,
+  "trained_at": 1761189181.4690864,
+  "trained_at_readable": "2025-10-23 03:13:01",
+  "samples_this_session": 36,
+  "new_rows_this_session": 2,
+  "trained_rows_total": 7394,
   "total_db_rows": 238453,
-  "percentage": 2.890716409523051,
+  "percentage": 3.1008207068059535,
   "final_loss": 0,
   "epochs": 3,
   "learning_rate": 5e-05,