Training update: 163,258/246,838 rows (66.14%) | +4 new @ 2025-12-30 04:18:17

README.md

@@ -25,7 +25,7 @@ pipeline_tag: fill-mask
 - Model type: fine-tuned lightweight BERT variant  
 - Languages: English & Indonesia
 - Finetuned from: `boltuix/bert-micro`
-- Status: **Early version** — trained on **99.45%** of planned data.
+- Status: **Early version** — trained on **66.14%** of planned data.
 
 **Model sources**  
 - Base model: [boltuix/bert-micro](https://huggingface.co/boltuix/bert-micro)
@@ -51,7 +51,7 @@ You can use this model to classify cybersecurity-related text — for example, w
 - Early classification of SIEM alert & events.
 
 ## 3. Bias, Risks, and Limitations 
-Because the model is based on a small subset (99.45%) 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 (66.14%) 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.**
 
@@ -75,9 +75,9 @@ Since cybersecurity data often contains lengthy alert descriptions and execution
 - **LR scheduler**: Linear with warmup
 
 ### Training Data
-- **Total database rows**: 164,092
-- **Rows processed (cumulative)**: 163,191 (99.45%)
-- **Training date**: 2025-11-13 07:05:35
+- **Total database rows**: 246,838
+- **Rows processed (cumulative)**: 163,258 (66.14%)
+- **Training date**: 2025-12-30 04:18:17
 
 ### Post-Training Metrics
 - **Final training loss**: 

config.json

@@ -17,7 +17,7 @@
   "num_hidden_layers": 2,
   "pad_token_id": 0,
   "position_embedding_type": "absolute",
-  "transformers_version": "4.57.1",
+  "transformers_version": "4.57.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 30522

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:26c5935114f21374e4d0f3f42a947134eefbe480e99aafb1d50a514e0acb719a
 size 17671560

training_args.bin

@@ -1,3 +1,3 @@
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-oid sha256:6cd3cce824720c4c5fc2824f84bc4ddec6fb7e00c59568f9c793c25db25817ec
+oid sha256:511d11caa4c0ce7dab8b27d22fce1367c2f561fb379e9c62e046686487c4e23d
 size 5905

training_metadata.json

@@ -1,11 +1,11 @@
 {
-  "trained_at": 1763017535.8335285,
-  "trained_at_readable": "2025-11-13 07:05:35",
-  "samples_this_session": 1510,
-  "new_rows_this_session": 67,
-  "trained_rows_total": 163191,
-  "total_db_rows": 164092,
-  "percentage": 99.45091777783195,
+  "trained_at": 1767068297.1543417,
+  "trained_at_readable": "2025-12-30 04:18:17",
+  "samples_this_session": 1491,
+  "new_rows_this_session": 4,
+  "trained_rows_total": 163258,
+  "total_db_rows": 246838,
+  "percentage": 66.13973537299768,
   "final_loss": 0,
   "epochs": 3,
   "learning_rate": 5e-05,