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Training update: 149,460/241,343 rows (61.93%) | +76 new @ 2025-11-03 04:05:21

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Files changed (4) hide show
  1. README.md +5 -5
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
  4. training_metadata.json +7 -7
README.md CHANGED
@@ -25,7 +25,7 @@ pipeline_tag: fill-mask
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  - Model type: fine-tuned lightweight BERT variant
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  - Languages: English & Indonesia
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  - Finetuned from: `boltuix/bert-micro`
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- - Status: **Early version** — trained on **61.88%** of planned data.
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  **Model sources**
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  - 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
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  - Early classification of SIEM alert & events.
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  ## 3. Bias, Risks, and Limitations
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- Because the model is based on a small subset (61.88%) of planned data, performance is preliminary and may degrade on unseen or specialized domains (industrial control, IoT logs, foreign language).
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  - 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.
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  - **Should not be used as sole authority for incident decisions; only as an aid to human analysts.**
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@@ -75,9 +75,9 @@ Since cybersecurity data often contains lengthy alert descriptions and execution
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  - **LR scheduler**: Linear with warmup
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  ### Training Data
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- - **Total database rows**: 241,330
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- - **Rows processed (cumulative)**: 149,331 (61.88%)
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- - **Training date**: 2025-11-03 03:02:37
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  ### Post-Training Metrics
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  - **Final training loss**:
 
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  - Model type: fine-tuned lightweight BERT variant
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  - Languages: English & Indonesia
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  - Finetuned from: `boltuix/bert-micro`
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+ - Status: **Early version** — trained on **61.93%** of planned data.
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  **Model sources**
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  - Base model: [boltuix/bert-micro](https://huggingface.co/boltuix/bert-micro)
 
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  - Early classification of SIEM alert & events.
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  ## 3. Bias, Risks, and Limitations
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+ Because the model is based on a small subset (61.93%) of planned data, performance is preliminary and may degrade on unseen or specialized domains (industrial control, IoT logs, foreign language).
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  - 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.
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  - **Should not be used as sole authority for incident decisions; only as an aid to human analysts.**
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  - **LR scheduler**: Linear with warmup
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  ### Training Data
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+ - **Total database rows**: 241,343
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+ - **Rows processed (cumulative)**: 149,460 (61.93%)
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+ - **Training date**: 2025-11-03 04:05:21
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  ### Post-Training Metrics
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  - **Final training loss**:
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training_metadata.json CHANGED
@@ -1,11 +1,11 @@
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  {
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- "trained_at": 1762138957.5496552,
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- "trained_at_readable": "2025-11-03 03:02:37",
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- "samples_this_session": 1492,
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- "new_rows_this_session": 129,
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- "trained_rows_total": 149331,
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- "total_db_rows": 241330,
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- "percentage": 61.87834086106162,
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  "final_loss": 0,
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  "epochs": 3,
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  "learning_rate": 5e-05,
 
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  {
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+ "trained_at": 1762142721.1101353,
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+ "trained_at_readable": "2025-11-03 04:05:21",
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+ "samples_this_session": 1500,
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+ "new_rows_this_session": 76,
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+ "trained_rows_total": 149460,
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+ "total_db_rows": 241343,
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+ "percentage": 61.92845866671086,
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  "final_loss": 0,
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  "epochs": 3,
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  "learning_rate": 5e-05,