R1-Responses to go from Unstructured to Structured

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license: apache-2.0
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# MasterControl AIML Team 🚀  

## Overview  
The **MasterControl AIML team** supports the **Hugging Face** initiative of re-creating **DeepSeek R1 training**, recognizing it as one of the most **impactful open-source projects** today.  

We aim to contribute to **reasoning datasets**, specifically those where:  
- A **real-world problem** involves **generating complex structured output**  
- It is accompanied by **step-by-step reasoning and unstructured input**  

## Challenges in Integrating Generative AI into Systems of Record (SoR)  
Integrating **Generative AI** into **Systems of Record (SoR)** for **health, life sciences, and manufacturing quality** is challenging due to:  
- These systems rely on **strictly structured data formats** (e.g., **JSON, XML, templates**).  
- **LLM outputs** are **unstructured** and do not conform to **regular expressions** or **context-free grammars**.  

## Techniques for Structured Output Generation  
To enforce structured output generation, we explore:  
- **Strict schema prompting**  
- **Post-processing and output validation**  
- **Reformulating text generation** into transitions between **finite-state machine states**  

## DeepSeek R1 Approach  
A key challenge is **fitting hybrid structured and unstructured historical manufacturing production records** to **master templates**.  
We aim to leverage the **DeepSeek R1 model**, which uses:  
- **Pure reinforcement learning** to train a **base language model**  
- Learning to **reason without human supervision**
   
## Model Used
- We used **Deepseek's Distilled 7b** to creating reasoning responses to go from unstructured to structured.

## Purpose of Reasoning Responses
- The reasoning responses are created in such a way, that if the model is presented with **unstructured text and a schema of rules, it needs to convert it into a structured schema**. These responses can be used for any unstructured to structured creation.

## Next Step: Reasoning Data  
Our **first step** is curating and contributing to **reasoning datasets** that facilitate structured output generation.  

## HF-Link
**https://huggingface.co/datasets/MasterControlAIML/R1-Reasoning-Unstructured-To-Structured**

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