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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
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