# smart-tab-grouping

**Repository Path**: mirrors_mozilla/smart-tab-grouping

## Basic Information

- **Project Name**: smart-tab-grouping
- **Description**: Code for tuning Smart Tab Grouping models for Firefox
- **Primary Language**: Unknown
- **License**: MPL-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-02-20
- **Last Updated**: 2026-03-29

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## Smart Tab Grouping

Smart Tab grouping encompasses:

    Suggesting a title for user created group of tabs

    Suggesting tabs from current window to be added to the current group

    Suggesting groups from current window [Currently out of Scope]

### Basic Architecture

Smart Tab grouping uses standard embedding models for grouping, and a fine tuned model for text generation.
![Smart Tab Grouping Diagram](images/smart-tab-grouping-diagram.png)

Notes on Diagram: All inference is in browser using the Firefox AI runtime and other local algorithms.

 ‘Distinct keywords’ are picked for inference using c-tf-idf algorithm, which finds relatively unique keywords in the title and description of the document with respect to the rest of the document.This helps distinguish what is unique about a group.

### Clustering tests

For interactive tests
```
streamlit run tab_grouping_streamlit.py

```
 

For batch testing of clustering methods:
```
python utils/grouping_pipeline.py

```

 
### Topic Name Data Generation Pipeline

![Smart Tab Grouping Diagram](images/synthetic-data-arch.png)

• Generate Archetypes and Synthetic Browsing History
``
   gen_annotation_data.py
``

• Preprocess Clusters as Client does
``
   tab_title_tuning_data.py
``

• Generate Labels
```
   tab_title_tuning_data.py
```

• Simplify Labels
```
   SimplifyMLTopics.ipynb
```

• Optional - Cluster Labels
```
   /analysis/Directed Training Clusters.ipynb
```
The clustering was used to generate some hints in the file 'topic_fine_tuning_data__01_05__grouped_with_hints.csv'
The hints provide n-Shot examples to help direct the labels for certain categories in the Generate Labels step.


•Fine tune the model, distill, quantize and export ML model
see src/jobs/Readme.md for details on this step

• Analyze Results of Topic Model
   /notebooks/Benchmarking.ipynb