README.md

---
language:
- en
- id
tags:
- bert
- text-classification
- token-classification
- cybersecurity
- fill-mask
- named-entity-recognition
base_model: boltuix/bert-micro
library_name: transformers
---
# bert-micro-cybersecurity

## 1. Model Details
**Model description**  
"bert-micro-cybersecurity" is a compact transformer model adapted for cybersecurity text classification tasks (e.g., threat detection, incident reports, malicious vs benign content).

- Model type: fine-tuned lightweight BERT variant  
- Languages: English & Indonesia
- Finetuned from: `boltuix/bert-micro`
- Status: **Early version** — trained on **0.68%** of planned data.

**Model sources**  
- Base model: [boltuix/bert-micro](https://huggingface.co/boltuix/bert-micro)
- Data: Cybersecurity Data

## 2. Uses
### Direct use  
You can use this model to classify cybersecurity-related text — for example, whether a given message, report or log entry indicates malicious intent, abnormal behaviour, or threat presence.

### Downstream use  
- Embedding extraction for clustering or anomaly detection in security logs.  
- As part of a pipeline for phishing detection, malicious email filtering, incident triage.  
- As a feature extractor feeding a downstream system (e.g., alert-generation, SOC dashboard).

### Out-of-scope use  
- Not meant for high-stakes automated blocking decisions without human review.  
- 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 (0.68%) 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  
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("codechrl/bert-micro-cybersecurity")
model = AutoModelForSequenceClassification.from_pretrained("codechrl/bert-micro-cybersecurity")

inputs = tokenizer("The server logged an unusual outbound connection to 123.123.123.123", 
                   return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)
logits = outputs.logits
predicted_class = logits.argmax(dim=-1).item()
```

## 5. Training Details
- **Trained records**: 1,614 / 237,699 (0.68%)
- **Learning rate**: 5e-05
- **Epochs**: 3
- **Batch size**: 1000
- **Max sequence length**: 512