---
license: other
license_name: link-attribution
license_link: https://dejanmarketing.com/link-attribution/
language:
- en
metrics:
- accuracy
- f1
- precision
- recall
base_model: microsoft/deberta-v3-large
pipeline_tag: text-classification
tags:
- grounding
- retrieval
- LLM-enhancement
- DejanAI
---

[![Dejan AI Logo](https://dejan.ai/wp-content/uploads/2024/02/dejan.png)](https://dejan.ai/blog/grounding-classifier/)

# Prompt Grounding Classifier

This model predicts whether a prompt **requires grounding** in external sources like web search, databases, or RAG pipelines.

It was fine-tuned from [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) using binary labels:

- `1` = grounding required  
- `0` = grounding not required

---

## 🚀 Use Case

This classifier acts as a **routing layer** in an LLM pipeline, helping decide:

- When to trigger retrieval  
- When to let the model respond from internal knowledge  
- How to optimize for latency and cost  

---

## 📦 Training Details

- Model: DeBERTa v3 Large  
- Fine-tuning: Full (no adapters)  
- Dropout: 0.1  
- Scheduler: Cosine with warmup  
- Batch size: 24 (accumulated)  
- Evaluation: every 500 steps  
- Metric used for best checkpoint: F1

---

## 🧪 Example Predictions

| Prompt                                                  | Grounding | Confidence |
|---------------------------------------------------------|-----------|------------|
| What’s the exchange rate for USD to Yen right now?      | 1         | 0.999      |
| Tell me a bedtime story about a robot and a dragon.     | 0         | 0.9961     |
| Who is the current CEO of Microsoft?                    | 1         | 0.9986     |

---

## 🧠 How to Use

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

model = AutoModelForSequenceClassification.from_pretrained("dejanseo/query-grounding")
tokenizer = AutoTokenizer.from_pretrained("dejanseo/query-grounding")

prompt = "What time is the next train from Tokyo to Osaka?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model(**inputs).logits
probs = F.softmax(outputs, dim=-1)
label = probs.argmax().item()
confidence = probs[0][label].item()
```

---

## 🧾 Dataset Origin

Prompts were collected using a Gemini 2.5 Pro + Google Search toolchain with grounding enabled. Each prompt's response was parsed to extract Gemini's grounding confidence, used as soft supervision for binary labeling:

- Label 1 if grounded confidence present
- Label 0 if response required no external evidence