---
license: apache-2.0
datasets:
- bravesoftware/diverse-llm-prompts-34k
- lmsys/lmsys-chat-1m
base_model:
- answerdotai/ModernBERT-base
---
# Androcles

Androcles is our inhouse **Prompt Classification Model**, used to classify user prompts into different categories to dynamically change the functionality based on the context of the request. It's a simple text classification model finetuned on a mix of synthetic & open source data across 21 labels 

**No Brave Browser/Leo user data was used to train this model - even if we wanted to, we don't have it**

## Labels
| Label               | Short Description                                                                 |
|---------------------|-----------------------------------------------------------------------------------|
| Brave               | The user is asking about Brave products — Brave Browser, Brave Search, Brave Rewards |
| Browser             | The user wants Leo to take actions in the browser — clicking, filling forms, navigating pages |
| Choice              | The user is deciding between two or more specific options and wants help choosing |
| Coding              | Anything related to coding — writing, reading, understanding, explaining, or running code in any language |
| Data Analysis       | The user has data they want analysed, visualised, or interpreted — CSV, JSON, a table, or raw numbers |
| Diagrams            | The user wants a diagram, flowchart, chart, or visual representation of a system or process |
| Fact Checking       | The user is asking whether a claim, statement, or piece of information is true or false |
| Finance             | The user is asking about money — currrency exchange/stock prices etc  |
| Image Generation    | Generating an image — the user is explicitly asking for an image to be created or produced |
| Math / Calculations | Anything involving numbers, arithmetic, algebra, statistics, or mathematical reasoning |
| Multilingualism     | The user is writing in or asking for a response in a non-English language |
| News                | The user is asking about current events, recent news, or what's happening in the world |
| Recommendation      | The user is asking for a recommendation — a product, service, tool, or option suited to their needs |
| Sports              | The user is asking about sports — scores, fixtures, standings, results, or sports news |
| Structured Writing  | The user wants help writing something structured and polished — a cover letter, essay, email, report, blog post etc |
| Summarisation       | The user wants a summary of something — an article, video, document, or piece of text |
| Thinking            | The user wants Leo to think through something carefully and methodically — a complex problem, a multi-step question, or a nuanced topic |
| Time-critical       | The request is urgent or time-sensitive — the user needs a fast answer, not a thorough one |
| Translation         | Translating text from one language to another |
| Travel Planning     | The user is planning a trip — flights, hotels, itineraries, destinations, things to do |
| Weather             | The user is asking about the weather — current conditions, forecasts, or climate for a location |

***

## Training

Androcles 2 was trained using a mix of [`bravesoftware/diverse-llm-prompts-34k`](https://huggingface.co/datasets/bravesoftware/androcles-2-chat-50k](https://huggingface.co/datasets/bravesoftware/diverse-llm-prompts-34k)) + labelled data from [`LMSYS/lmsys-chat-1m`](https://huggingface.co/datasets/lmsys/lmsys-chat-1m)

The scripts for training the model & generating the initial dataset can be found [here](https://github.com/brave/Androcles/tree/main)

## How We Use It

We use Androcles in a couple of different ways:

* As a first step in our [AI Gateway Server](https://github.com/brave/ai-gateway/tree/main) powering [Leo](https://brave.com/leo/), classifying prompts into categories with the aim of dynamically changing available tools/instructions depending on what the user asks for

* As a 'primary category' labeller for our privacy-preserving Leo Analytics server

We host it using Nvidia's Triton Inference Server to serve it with low-latency at scale, but we've also used KServe in the past

## Caveats

This model was not trained to be perfect and will make mistakes in classification, the aim was to catch some low-hanging fruit semantically to improve the user experience