VerifiedPrompts/CNTXT-Filter-Prompt-Opt

📘 Model Card: CNTXT-Filter-Prompt-Opt

🔍 Model Overview

CNTXT-Filter-Prompt-Opt is a lightweight, high-accuracy text classification model designed to evaluate the contextual completeness of user prompts submitted to LLMs. It acts as a gatekeeper before generation, helping eliminate vague or spam-like input and ensuring only quality prompts proceed to LLM2.

• —Base model: distilbert-base-uncased
• —Trained on: 200k labeled prompts
• —Purpose: Prompt validation, spam filtering, and context enforcement

🎯 Intended Use

This model is intended for:

• —Pre-processing prompts before LLM2 generation
• —Blocking unclear or context-poor requests
• —Structuring user input pipelines in AI apps, bots, and assistants

🔢 Labels

The model classifies prompts into 3 categories:

📊 Training Details

• —Model: distilbert-base-uncased
• —Training method: Hugging Face AutoTrain
• —Dataset size: 200,000 prompts (curated, curriculum style)
• —Epochs: 3
• —Batch size: 8
• —Max seq length: 128
• —Mixed Precision: fp16
• —LoRA: ❌ Disabled
• —Optimizer: AdamW

✅ Evaluation

The model generalizes extremely well on all validation samples.

⚙️ How to Use

from transformers import pipeline

classifier = pipeline("text-classification", model="VerifiedPrompts/CNTXT-Filter-Prompt-Opt")
prompt = "Write a business plan for a freelance app in Canada."
result = classifier(prompt)

print(result)
# [{'label': 'has context', 'score': 0.98}]