Update README.md

README.md

@@ -1,18 +1,90 @@
-📘 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 prompts submitted to large language models (LLMs). It serves as the first gatekeeper in a multi-stage moderation and optimization pipeline. Built on `distilbert-base-uncased`, it classifies prompts into three context categories, allowing or blocking them before LLM2 generation is triggered.
-🎯 Intended Use
-This model is used as a real-time filter in AI-powered systems to:
- Block unclear or vague prompts
- Identify missing key information (e.g., platform, audience, budget)
- Allow only context-rich prompts to pass to downstream models (LLM2)
-🔢 Labels & Meanings
-The model is trained to classify prompts into the following classes:
- `has context` — prompt is clear, actionable, and well-defined
- `missing platform, audience, budget, goal` — prompt lacks structural clarity
- `Intent is unclear, Please input more context` — prompt is vague or spam-like
-📊 Training Details
-Trained on a curated dataset of 200,000 prompts using Hugging Face AutoTrain with LoRA disabled. Class balance and curriculum learning were applied: fully positive prompts first, followed by negative and vague examples.

Key parameters:
- Model: distilbert-base-uncased
- Max sequence length: 128
- Batch size: 8
- Epochs: 3
- Optimizer: AdamW
- Learning rate: 5e-5
- Mixed precision: FP16
- Accuracy: 100% (training + validation)
-✅ Evaluation Metrics
-The model achieved perfect scores on the evaluation set:
- Accuracy: 1.0
- F1 Score (macro/micro/weighted): 1.0
- Precision & Recall: 1.0
This reflects perfect generalization within the prompt context classification task.
-⚙️ How to Use
-Using the `transformers` library:
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="VerifiedPrompts/CNTXT-Filter-Prompt-Opt")
result = classifier("Write an advertising plan for an eco product in Canada.")
print(result)
# → [{'label': 'has context', 'score': 0.97}]
```
-🧱 Role in AI Pipeline
-This model should be deployed at the start of any user-to-AI interaction system. It works alongside:
- OpenAI’s Moderation API (for abuse/harm filtering)
- LLM2 (like GPT-3.5 or Mistral) which executes generation if context is valid
- Prompt optimizers, feedback systems, or analytics dashboards
-⚖️ License & Ownership
-MIT License — freely reusable for research, commercial, or operational deployment.
Developed and owned by VerifiedPrompts.
-
🗓️ Last updated: May 27, 2025
+---
+license: mit
+tags:
+  - text-classification
+  - prompt-powered systems to:
- Block unclear or vague prompts
- Identify missing key information (e.g., platform, audience, budget)
- Allow only context-rich prompts to pass to downstream models (LLM2)filtering
+  - moderation
+The model is trained to classify prompts into the following classes:
  - `has context` — prompt is clear, actionable, and well-defined
- `missing platform, audience, budget, goal` — prompt lacks structural clarity
- `Intent is unclear, Please input more context` — prompt is vague or spam-likedistilbert
+  - transformers
+datasets: fully positive prompts first, followed by negative and vague examples.

Key parameters:
- Model: distilbert-base-uncased
- Max sequence length: 128
- Batch size: 8
- Epochs: 3
- Optimizer: AdamW
- Learning rate: 5e-5
- Mixed precision: FP16
- Accuracy: 100% (training + validation)
+  - VerifiedPrompts/cntxt-class-final
+language:
- Accuracy: 1.0
- F1 Score (macro/micro/weighted): 1.0
- Precision & Recall: 1.0
This reflects perfect generalization within the prompt context classification task.
+  - en
+pipeline_tag:
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="VerifiedPrompts/CNTXT-Filter-Prompt-Opt")
result = classifier("Write an advertising plan for an eco product in Canada.")
print(result)
# → [{'label': 'has context', 'score': 0.97}]
```
+widget:
+  - text: "Write a LinkedIn post about eco-friendly tech system. It works alongside:
- OpenAI’s Moderation API (for Gen filtering)
-Z entrepreneurs.5 or Mistral) which executes generation if context is valid
- Prompt optimizers, feedback systems, or analytics dashboards"
+    example_title: Context-rich prompt
+  - text: "Write freely reusable for research, commercial, or operational deployment.
Developed and owned by VerifiedPrompts.something"
+
🗓️ Last updated    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}]