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---
license: mit
tags:
- text-classification
- prompt-filtering
- moderation
- distilbert
- transformers
datasets:
- VerifiedPrompts/cntxt-class-final
language:
- en
pipeline_tag: text-classification
widget:
- text: "Write a LinkedIn post about eco-friendly tech for Gen Z entrepreneurs."
example_title: Context-rich prompt
- text: "Write something"
example_title: Vague prompt
---
# 📘 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:
| 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 |
---
## 📊 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
| 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.
---
## ⚙️ How to Use
```python
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}]
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