# smart_intent

**Repository Path**: mirrors_mozilla/smart_intent

## Basic Information

- **Project Name**: smart_intent
- **Description**: queries to intent
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-09-30
- **Last Updated**: 2026-02-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## smart_intent
Classify query to various intents as below
1) `information_intent`: intention to learn more about something. Eg. what is programming?, How does docking station work ?
2) `yelp_intent`: intention to look for services/local businesses. Eg. italian food near me, floor repair cost
3) `navigation_intent`: intention to navigate from search to the website. Eg. email login, bank branch routing number
4) `travel_intent`: intention to travel. Eg. hotels in Paris, cruise trip to carribean islands
5) `purchase_intent`: intention to purchase products. Eg. price of us open tennis tickets, buy iphone 10
6) `weather_intent`: to know the weather or temperature of a city. Eg. weather in Miami, temperature in Seattle
7) `translation_intent`: to translate from and to various languages. Eg. what is hello in spanish, translate hi to japanese
8) `unknown`: when the intent is inconclusive

## Steps to install the App 
1) clone the repo
2) python -m venv venv
3) source /venv/bin/activate
4) python -m pip install -r requirements
5) streamlit run src/intent_app.py

<img width="1586" alt="image" src="https://github.com/user-attachments/assets/c5b1931c-fa9a-47ad-a229-412610b83910">

## steps to run the sample intent inference
steps 1 - 4 from above
5) python src/infer_intent.py

## steps to train intent classifier model
`data preparation`:
   ```
   python src/data_prep_intent.py
   ```
`intent model training`:
  ```
  python src/train_intent.py
  ```
(optional)
`upload the model to hub`:
  ```
  python src/upload_intent.py
  ```

## steps to train NER model
`data preparation`:
   ```
   python src/data_prep_ner.py
   ```
`NER model training`:
  ```
  python src/train_ner.py
  ```
(optional)
`upload the model to hub`:
  ```
  python src/upload_ner.py
  ```

Steps to quantize:
```
venv/bin/python convert.py --model_id Mozilla/mobilebert-uncased-finetuned-LoRA-intent-classifier --quantize --modes q4 q8 fp16 --task text-classification  --output_parent_dir output_models

venv/bin/python convert.py --model_id Mozilla/distilbert-uncased-NER-LoRA --quantize --modes q4 q8 fp16 --task token-classification  --output_parent_dir output_models
```