| ---
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| pipeline_tag: text-classification
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| library_name: transformers
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| license: mit
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| ---
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| # Query Complexity Classifier
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| This model classifies user queries based on their **complexity level** so they can be routed to an appropriate Large Language Model (LLM).
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| The model predicts three classes:
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| * **Simple**
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| * **Medium**
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| * **Complex**
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| It can be used as a **pre-routing layer** in AI systems where different LLMs handle different levels of query complexity.
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| ---
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| ## Model
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| Base Model: DistilBERT
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| Task: Text Classification (3 classes)
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|
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| ---
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| ## Download and Use
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| You can download and load the model directly from Hugging Face using the `transformers` library.
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| ### Install dependencies
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| ```bash
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| pip install transformers torch
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| ```
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| ### Load the model
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| ```python
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| from transformers import AutoTokenizer, AutoModelForSequenceClassification
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| import torch
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| model_name = "Shaheer001/Query-Complexity-Classifier"
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| tokenizer = AutoTokenizer.from_pretrained(model_name)
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| model = AutoModelForSequenceClassification.from_pretrained(model_name)
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| ```
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| ### Run inference
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| ```python
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| text = "Explain how Kubernetes architecture works."
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| inputs = tokenizer(text, return_tensors="pt", truncation=True)
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| outputs = model(**inputs)
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| prediction = torch.argmax(outputs.logits, dim=1).item()
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| labels = ["Simple", "Medium", "Complex"]
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| print("Predicted Complexity:", labels[prediction])
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| ```
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| ---
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| ## Example
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| Input:
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| ```
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| Explain Kubernetes architecture
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| ```
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| Output:
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| ```
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| Complex
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| ```
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| ---
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| ## Use Case
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| This model can be used to build **LLM routing systems** where queries are automatically sent to different language models depending on their complexity.
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| Example workflow:
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| User Query → Complexity Classifier → LLM Router → Selected LLM
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|
