📘 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

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🎯 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

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🔢 Labels

The model classifies prompts into 3 categories:

Label	Description
has context	Prompt is clear, actionable, and self-contained
missing platform, audience, budget, goal	Prompt lacks structural clarity
Intent is unclear, Please input more context	Vague or incoherent prompt

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📊 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

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✅ Evaluation

Metric	Score
Accuracy	1.0
F1 (macro/micro/weighted)	1.0
Precision / Recall	1.0
Validation Loss	0.0

The model generalizes extremely well on all validation samples.

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⚙️ 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}]