Shell Command Classifier
A fast binary classifier that distinguishes shell commands from natural language queries.
Performance
| Metric | Value |
|---|---|
| Accuracy | 100% |
| F1 Score | 1.0 |
| Latency | ~3ms |
| Model Size | 0.8MB (ONNX) |
Usage
Python (ONNX)
import onnxruntime as ort
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("elleryfamilia/broshky")
session = ort.InferenceSession("onnx/model.onnx")
text = "ls -la"
inputs = tokenizer(text, return_tensors="np", padding=True, truncation=True, max_length=128)
outputs = session.run(None, {
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"]
})
# outputs[0] contains logits: [natural_language_score, command_score]
Python (Transformers)
from transformers import pipeline
classifier = pipeline("text-classification", model="elleryfamilia/broshky")
result = classifier("git commit -m 'fix bug'")
# [{'label': 'command', 'score': 0.99}]
Labels
0/natural_language: Natural language queries (e.g., "how do I list files?")1/command: Shell commands (e.g., "ls -la", "git status")
Training
- Base model:
sentence-transformers/all-MiniLM-L6-v2 - Training data: ~10K synthetic examples generated with Kimi K2.5
- Fine-tuned for 3 epochs on Modal (T4 GPU)
License
MIT
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