---
pipeline_tag: text-classification
library_name: transformers
license: mit
---

# Query Complexity Classifier

This model classifies user queries based on their **complexity level** so they can be routed to an appropriate Large Language Model (LLM).

The model predicts three classes:

* **Simple**
* **Medium**
* **Complex**

It can be used as a **pre-routing layer** in AI systems where different LLMs handle different levels of query complexity.

---

## Model

Base Model: DistilBERT
Task: Text Classification (3 classes)

---

## Download and Use

You can download and load the model directly from Hugging Face using the `transformers` library.

### Install dependencies

```bash
pip install transformers torch
```

### Load the model

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "Shaheer001/Query-Complexity-Classifier"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
```

### Run inference

```python
text = "Explain how Kubernetes architecture works."

inputs = tokenizer(text, return_tensors="pt", truncation=True)

outputs = model(**inputs)

prediction = torch.argmax(outputs.logits, dim=1).item()

labels = ["Simple", "Medium", "Complex"]

print("Predicted Complexity:", labels[prediction])
```

---

## Example

Input:

```
Explain Kubernetes architecture
```

Output:

```
Complex
```

---

## Use Case

This model can be used to build **LLM routing systems** where queries are automatically sent to different language models depending on their complexity.

Example workflow:

User Query → Complexity Classifier → LLM Router → Selected LLM