cngchis/phi4-mini-intent

Intent Classification Model

Model Description

This repository contains a fine-tuned Transformer model for intent classification.

The model is built using Hugging Face transformers and stored in safetensors format, enabling efficient and safe loading.

It predicts an intent label from input text for tasks such as chatbot understanding, ticket routing, and text categorization.

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How to Use

Install dependencies

pip install transformers==4.57.6

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Load model

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_path = "cngchis/phi4-mini-intent"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)

text = "I cannot log into my account"

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

with torch.no_grad():
    outputs = model(**inputs)

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

print(predicted_class)

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Input Format (Recommended)

"I want to reset my password"

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Output Format

The model outputs a class index, which can be mapped to intent labels:

3  → password_reset
1  → login_issue
5  → payment_problem

(You should define label mapping in your application.)

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Model Details

• Architecture: Transformer-based classification model
• Task: Intent classification
• Format: PyTorch (safetensors)
• Library: Hugging Face Transformers
• Input: Natural language text
• Output: Single intent class

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Notes

• Best performance when input format matches training data
• Requires label mapping for interpretation
• Works with GPU
• Supports batch inference via Transformers

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Limitations

Not suitable for generative tasks Sensitive to domain shift (out-of-distribution text) Requires consistent intent label schema

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Acknowledgements

Built using:

Hugging Face Transformers PyTorch Safetensors format