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Training update: 4,416/238,152 rows (1.85%) | +100 new @ 2025-10-22 03:30:26

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Files changed (4) hide show
  1. README.md +3 -3
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
  4. training_metadata.json +6 -6
README.md CHANGED
@@ -21,7 +21,7 @@ library_name: transformers
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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 **1.81%** of planned data.
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  **Model sources**
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  - Base model: [boltuix/bert-micro](https://huggingface.co/boltuix/bert-micro)
@@ -42,7 +42,7 @@ You can use this model to classify cybersecurity-related text — for example, w
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  - Not tested for non-cybersecurity domains or out-of-distribution data.
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  ## 3. Bias, Risks, and Limitations
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- Because the model is based on a small subset (1.81%) 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.
@@ -62,7 +62,7 @@ predicted_class = logits.argmax(dim=-1).item()
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  ```
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  ## 5. Training Details
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- - **Trained records**: 4,316 / 238,128 (1.81%)
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  - **Learning rate**: 5e-05
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  - **Epochs**: 3
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  - **Batch size**: 16
 
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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 **1.85%** 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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  - Not tested for non-cybersecurity domains or out-of-distribution data.
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  ## 3. Bias, Risks, and Limitations
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+ Because the model is based on a small subset (1.85%) 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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  ```
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  ## 5. Training Details
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+ - **Trained records**: 4,416 / 238,152 (1.85%)
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  - **Learning rate**: 5e-05
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  - **Epochs**: 3
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  - **Batch size**: 16
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training_metadata.json CHANGED
@@ -1,11 +1,11 @@
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  {
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- "trained_at": 1761097612.9782152,
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- "trained_at_readable": "2025-10-22 01:46:52",
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- "samples_this_session": 100,
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  "new_rows_this_session": 100,
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- "trained_rows_total": 4316,
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- "total_db_rows": 238128,
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- "percentage": 1.8124706040448832,
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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": 1761103826.7234476,
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+ "trained_at_readable": "2025-10-22 03:30:26",
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+ "samples_this_session": 7671,
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  "new_rows_this_session": 100,
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+ "trained_rows_total": 4416,
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+ "total_db_rows": 238152,
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+ "percentage": 1.8542779401390708,
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  "final_loss": 0,
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  "learning_rate": 5e-05