---
language:
- en
- id
tags:
- text-classification
- cybersecurity
base_model: boltuix/bert-micro
---

# Model Card for “bert-micro-cybersecurity”

## 1. Model Details

**Model description**  
“bert-micro-cybersecurity” is a compact transformer model derived from `boltuix/bert-micro`, 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 ~ **2%** of planned data.

**Model sources**  
- Base model: [boltuix/bert-micro](https://huggingface.co/boltuix/bert-micro) :contentReference[oaicite:3]{index=3}  
- 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.  
- Not tested for non-cybersecurity domains or out-of-distribution data.

## 3. Bias, Risks, and Limitations 
Because the model is based on a very small subset (~ 2%) 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. :contentReference[oaicite:4]{index=4}  
- 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("your-username/bert-micro-cybersecurity")
model = AutoModelForSequenceClassification.from_pretrained("your-username/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()