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rushi-shaharao/
distilbert-prompt-classifier

Name: distilbert-prompt-classifier
Availability: InStock

prompt-classification

user-vs-system

autotrain_compatible

endpoints_compatible

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🧠 DistilBERT Prompt Classifier

This is a fine-tuned DistilBERT model for classifying prompt types as either "user prompt" or "system prompt". It is useful for distinguishing between different roles in conversation-based datasets like those used in chatbots, assistants, or instruction tuning.

✨ Model Details

Model Name: distilbert-prompt-classifier
Developed by: Mayuresh Mane
Base Model: distilbert-base-uncased
Task: Text Classification (Binary)
Labels: 0 = system prompt, 1 = user prompt
Language: English
License: Apache 2.0
Framework: 🤗 Transformers

🔗 Model Sources

Model Hub: rushi-shaharao/distilbert-prompt-classifier

💡 Uses

✅ Direct Use

You can use this model to classify any single prompt into either a system or user prompt.

🚫 Out-of-Scope Use

Not intended for multi-language prompt classification.
May not generalize well to noisy or adversarial text outside of prompt-type formatting.

🧪 How to Use

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import torch.nn.functional as F

model_name = "rushi-shaharao/distilbert-prompt-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

prompt = "You are a helpful assistant."
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, padding=True)

with torch.no_grad():
    outputs = model(**inputs)
    probs = F.softmax(outputs.logits, dim=1)
    predicted_class = torch.argmax(probs, dim=1).item()

label_map = {0: "system prompt", 1: "user prompt"}
print(f"Predicted: {label_map[predicted_class]} ({probs[0][predicted_class]:.2f} confidence)")