Training update: 163,113/164,092 rows (99.40%) | +10 new @ 2025-11-12 17:58:58
Browse files- README.md +5 -5
- model.safetensors +1 -1
- training_args.bin +1 -1
- training_metadata.json +7 -7
README.md
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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 **99.
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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 (99.
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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**: 164,
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- **Rows processed (cumulative)**: 163,
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- **Training date**: 2025-11-12 17:
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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 **99.40%** 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 (99.40%) 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**: 164,092
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- **Rows processed (cumulative)**: 163,113 (99.40%)
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- **Training date**: 2025-11-12 17:58:58
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### Post-Training Metrics
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- **Final training loss**:
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model.safetensors
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training_args.bin
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training_metadata.json
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{
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"trained_at":
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"trained_at_readable": "2025-11-12 17:
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"samples_this_session":
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"new_rows_this_session":
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"trained_rows_total":
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"total_db_rows":
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"percentage": 99.
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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_readable": "2025-11-12 17:58:58",
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"samples_this_session": 1500,
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"new_rows_this_session": 10,
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"trained_rows_total": 163113,
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"total_db_rows": 164092,
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"percentage": 99.40338346781074,
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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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