| --- |
| license: mit |
| datasets: |
| - cngchis/Support-Ticket-Router-12K-Cleaned |
| language: |
| - en |
| metrics: |
| - f1 |
| - accuracy |
| - precision |
| - recall |
| base_model: |
| - unsloth/Phi-4-mini-instruct |
| new_version: cngchis/phi4-mini-intent |
| pipeline_tag: text-classification |
| library_name: transformers |
| --- |
| |
| # 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. |
|
|
| --- |
|
|
| ## How to Use |
|
|
| ### Install dependencies |
|
|
| ```bash |
| pip install transformers==4.57.6 |
| ``` |
|
|
| --- |
|
|
| ### Load model |
| ```bash |
| 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) |
| ``` |
|
|
| --- |
|
|
| ### Input Format (Recommended) |
|
|
| ```json |
| "I want to reset my password" |
| ``` |
|
|
| --- |
|
|
| ### Output Format |
|
|
| The model outputs a class index, which can be mapped to intent labels: |
|
|
| ```json |
| 3 → password_reset |
| 1 → login_issue |
| 5 → payment_problem |
| ``` |
|
|
| (You should define label mapping in your application.) |
|
|
| --- |
|
|
| ### 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 |
|
|
| --- |
|
|
| ### Notes |
| - Best performance when input format matches training data |
| - Requires label mapping for interpretation |
| - Works with GPU |
| - Supports batch inference via Transformers |
|
|
| --- |
|
|
| ### Limitations |
|
|
| Not suitable for generative tasks |
| Sensitive to domain shift (out-of-distribution text) |
| Requires consistent intent label schema |
|
|
| --- |
|
|
| ### Acknowledgements |
|
|
| Built using: |
|
|
| Hugging Face Transformers |
| PyTorch |
| Safetensors format |
|
|
|
|
