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ovinduG/
multi-domain-classifier-phi3

Name: multi-domain-classifier-phi3
Availability: InStock

domain-classification

custom

apache-2.0

endpoints_compatible

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Multi-Domain Classifier (Phi-3)

A fine-tuned domain classification model based on Microsoft's Phi-3-mini-4k-instruct, trained to classify queries into 17 domains and detect multi-domain queries.

Model Description

This model classifies text queries into domains and identifies when queries span multiple domains. It returns structured JSON output with primary domain, confidence scores, multi-domain flag, and secondary domains.

Supported Domains (17 total)

ambiguous, api_generation, business, coding, creative_content, data_analysis, education, general_knowledge, geography, history, law, literature, mathematics, medicine, science, sensitive, technology

Training Details

Base Model: microsoft/Phi-3-mini-4k-instruct
Training Method: LoRA (Low-Rank Adaptation)
Training Samples: 3,666
Multi-Domain Samples: 516 (14.1%)
Training Time: 1.61 hours
LoRA Rank: 32
LoRA Alpha: 64

Training Configuration

python

{
  "num_epochs": 5,
  "batch_size": 16,
  "learning_rate": 0.0002,
  "warmup_ratio": 0.1,
  "weight_decay": 0.01,
  "gradient_accumulation_steps": 2,
  "eval_steps": 50,
  "save_steps": 100,
  "logging_steps": 10
}

Performance

Primary Domain Accuracy: 97.25%
F1 Score (Macro): 0.9177458758572196
Multi-Domain Detection F1: 0.9523809523809523

Usage

Installation

pip install transformers torch peft

Basic Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
import json

# Load model
base_model = AutoModelForCausalLM.from_pretrained(
    "microsoft/Phi-3-mini-4k-instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

model = PeftModel.from_pretrained(base_model, "ovinduG/multi-domain-classifier-phi3")
tokenizer = AutoTokenizer.from_pretrained("ovinduG/multi-domain-classifier-phi3")

# Prepare input
query = "Build a machine learning model to analyze sales data"

prompt = f'''Classify this query: {query}

Output JSON format:
{
  "primary_domain": "domain_name",
  "primary_confidence": 0.95,
  "is_multi_domain": true/false,
  "secondary_domains": [{"domain": "name", "confidence": 0.85}]
}'''

# Generate
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.1)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

# Parse result
result = json.loads(response.split("Output JSON format:")[-1].strip())
print(result)

Output Format

json

{
  "primary_domain": "data_analysis",
  "primary_confidence": 0.92,
  "is_multi_domain": true,
  "secondary_domains": [
    {"domain": "machine_learning", "confidence": 0.85},
    {"domain": "business", "confidence": 0.72}
  ]
}

Use Cases

Query Routing: Route user queries to specialized models or APIs based on domain
Intent Classification: Understand the domain and complexity of user requests
Multi-Domain Detection: Identify queries that require expertise in multiple areas
Content Categorization: Classify documents and articles by domain

Limitations

Model may struggle with very rare domains (< 50 training examples)
Performance may degrade on domains not seen during training
Confidence scores are relative and may need calibration for production use
Secondary domain detection accuracy varies based on query clarity

Training Data

Trained on a custom dataset of 3,666 queries across 17 domains, with 14.1% multi-domain examples.

Citation

bibtex

@misc{multi-domain-classifier-phi3,
  author = {ovinduG},
  title = {Multi-Domain Classifier based on Phi-3},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/ovinduG/multi-domain-classifier-phi3}
}

Model Card Authors

ovinduG

License

Apache 2.0 (following base model license)