Update README.md

README.md

@@ -4,4 +4,57 @@ datasets:
 - bravesoftware/androcles-2-chat-50k
 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 — current conditions, forecasts, or climate for a location *(Note: This seems misaligned; likely refers to financial data like stock prices, crypto, exchange rates)* |
+| 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 https://huggingface.co/datasets/bravesoftware/androcles-2-chat-50k
+
+The scripts for training the model & generating the initial dataset can be found here [TBA]
+
+## How We Use It
+
+We use Androcles in a couple of different ways:
+
+* As a first step in our Leo backend server [TBA], classifying prompts into categories with the aim of dynamically changing available tools/instructions depending on what the user asks for - See here [tba]
+* 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
+
+