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| Metric | Value |
|---|---|
| Accuracy | 100% |
| F1 Score | 1.0 |
| Latency | ~3ms |
| Model Size | 0.8MB (ONNX) |
1import onnxruntime as ort
2from transformers import AutoTokenizer
3
4tokenizer = AutoTokenizer.from_pretrained("elleryfamilia/broshky")
5session = ort.InferenceSession("onnx/model.onnx")
6
7text = "ls -la"
8inputs = tokenizer(text, return_tensors="np", padding=True, truncation=True, max_length=128)
9outputs = session.run(None, {
10 "input_ids": inputs["input_ids"],
11 "attention_mask": inputs["attention_mask"]
12})
13# outputs[0] contains logits: [natural_language_score, command_score]1from transformers import pipeline
2
3classifier = pipeline("text-classification", model="elleryfamilia/broshky")
4result = classifier("git commit -m 'fix bug'")
5# [{'label': 'command', 'score': 0.99}]0 / natural_language: Natural language queries (e.g., "how do I list files?")1 / command: Shell commands (e.g., "ls -la", "git status")sentence-transformers/all-MiniLM-L6-v2