Training update: 6,788/238,451 rows (2.85%) | +5 new @ 2025-10-23 02:48:14

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 **2.84%** of planned data.
+- Status: **Early version** — trained on **2.85%** 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 (2.84%) 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 (2.85%) 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**: 238,451
-- **Rows processed (cumulative)**: 6,763 (2.84%)
-- **Rows in this session**: 25
-- **Training samples (after chunking)**: 25
-- **Training date**: 2025-10-23 02:45:29
+- **Rows processed (cumulative)**: 6,788 (2.85%)
+- **Rows in this session**: 5
+- **Training samples (after chunking)**: 28
+- **Training date**: 2025-10-23 02:48:14
 
 ### Post-Training Metrics
 - **Final training loss**: 0.0000
-- **Rows→Samples ratio**: 1.00x (average chunks per row)
+- **Rows→Samples ratio**: 5.60x (average chunks per row)

config.json

@@ -17,7 +17,7 @@
   "num_hidden_layers": 2,
   "pad_token_id": 0,
   "position_embedding_type": "absolute",
-  "transformers_version": "4.57.0",
+  "transformers_version": "4.57.1",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 30522

model.safetensors

@@ -1,3 +1,3 @@
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-oid sha256:983be3a2e9678daa96170b0969607343ac60dacf50afe2de4005fbdbb918d54d
+oid sha256:b491119a6ad59eaf03a443d49a6f86d14c3db6e0a46ba05e54d82c319e907689
 size 17671560

training_args.bin

@@ -1,3 +1,3 @@
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-oid sha256:8ef5380f08e852850356b566ce57de8329dc2f88e4f45d0840e444f505dbe40b
+oid sha256:8004239770512ae458a8e909f7ac7c389c458a70bb8f5d47a2e8d72f65b2482d
 size 5905

training_metadata.json

@@ -1,11 +1,11 @@
 {
-  "trained_at": 1761187529.3583431,
-  "trained_at_readable": "2025-10-23 02:45:29",
-  "samples_this_session": 25,
-  "new_rows_this_session": 25,
-  "trained_rows_total": 6763,
+  "trained_at": 1761187694.9856818,
+  "trained_at_readable": "2025-10-23 02:48:14",
+  "samples_this_session": 28,
+  "new_rows_this_session": 5,
+  "trained_rows_total": 6788,
   "total_db_rows": 238451,
-  "percentage": 2.8362221169129085,
+  "percentage": 2.846706451220586,
   "final_loss": 0,
   "epochs": 3,
   "learning_rate": 5e-05,