Merge pull request #370 from raosukrit67/main

Refactored NDD Community Notebook: Forms-W2 for v25.10

Author: kirit93

nemo/NeMo-Data-Designer/self-hosted-tutorials/community-contributions/README.md

@@ -16,16 +16,16 @@
 
 | Notebook                                          | Domain              | Description                                                     |
 |---------------------------------------------------|---------------------|-----------------------------------------------------------------|
-| [person-sampler-tutorial.ipynb](./advanced/person-samplers/person-sampler-tutorial.ipynb) | Persona Samplers    | Generate realistic personas using the person sampler |
-| [clinical-trials.ipynb](./advanced/healthcare-datasets/clinical-trials.ipynb) | Healthcare          | Build synthetic clinical trial datasets with realistic PII for testing data protection |
-| [insurance-claims.ipynb](./advanced/healthcare-datasets/insurance-claims.ipynb) | Healthcare          | Create synthetic insurance claims datasets with realistic claim data and processing information |
-| [physician-notes-with-realistic-personal-details.ipynb](./advanced/healthcare-datasets/physician-notes-with-realistic-personal-details.ipynb) | Healthcare          | Generate realistic patient data and physician notes with embedded personal information |
-| [w2-dataset.ipynb](./advanced/forms/w2-dataset.ipynb) | Forms & Documents   | Generate synthetic W-2 tax form datasets with realistic employee and employer information |
-| [multi-turn-conversation.ipynb](./advanced/multi-turn-chat/multi-turn-conversation.ipynb) | Conversational AI   | Build synthetic conversational data with realistic person details and multi-turn dialogues |
-| [visual-question-answering-using-vlm.ipynb](./advanced/multimodal/visual-question-answering-using-vlm.ipynb) | Multimodal          | Create visual question answering datasets using Vision Language Models |
-| [product-question-answer-generator.ipynb](./advanced/qa-generation/product-question-answer-generator.ipynb) | Q&A Generation      | Build product information datasets with corresponding questions and answers |
-| [generate-rag-evaluation-dataset.ipynb](./advanced/rag-examples/generate-rag-evaluation-dataset.ipynb) | RAG & Retrieval     | Generate diverse RAG evaluation datasets for testing retrieval-augmented generation systems |
-| [reasoning-traces.ipynb](./advanced/reasoning/reasoning-traces.ipynb) | Reasoning           | Build synthetic reasoning traces to demonstrate step-by-step problem-solving processes |
-| [text-to-python.ipynb](./advanced/text-to-code/text-to-python.ipynb) | Text-to-Code        | Generate Python code from natural language instructions with validation and evaluation |
-| [text-to-python-evol.ipynb](./advanced/text-to-code/text-to-python-evol.ipynb) | Text-to-Code        | Build advanced Python code generation with evolutionary improvements and iterative refinement |
-| [text-to-sql.ipynb](./advanced/text-to-code/text-to-sql.ipynb) | Text-to-Code        | Create SQL queries from natural language descriptions with validation and testing |
+| [person-sampler-tutorial.ipynb](./person-samplers/person-sampler-tutorial.ipynb) | Persona Samplers    | Generate realistic personas using the person sampler |
+| [clinical-trials.ipynb](./healthcare-datasets/clinical-trials.ipynb) | Healthcare          | Build synthetic clinical trial datasets with realistic PII for testing data protection |
+| [insurance-claims.ipynb](./healthcare-datasets/insurance-claims.ipynb) | Healthcare          | Create synthetic insurance claims datasets with realistic claim data and processing information |
+| [physician-notes-with-realistic-personal-details.ipynb](./healthcare-datasets/physician-notes-with-realistic-personal-details.ipynb) | Healthcare          | Generate realistic patient data and physician notes with embedded personal information |
+| [w2-dataset.ipynb](./forms/w2-dataset.ipynb) | Forms & Documents   | Generate synthetic W-2 tax form datasets with realistic employee and employer information |
+| [multi-turn-conversation.ipynb](./multi-turn-chat/multi-turn-conversation.ipynb) | Conversational AI   | Build synthetic conversational data with realistic person details and multi-turn dialogues |
+| [visual-question-answering-using-vlm.ipynb](./multimodal/visual-question-answering-using-vlm.ipynb) | Multimodal          | Create visual question answering datasets using Vision Language Models |
+| [product-question-answer-generator.ipynb](./qa-generation/product-question-answer-generator.ipynb) | Q&A Generation      | Build product information datasets with corresponding questions and answers |
+| [generate-rag-evaluation-dataset.ipynb](./rag-examples/generate-rag-evaluation-dataset.ipynb) | RAG & Retrieval     | Generate diverse RAG evaluation datasets for testing retrieval-augmented generation systems |
+| [reasoning-traces.ipynb](./reasoning/reasoning-traces.ipynb) | Reasoning           | Build synthetic reasoning traces to demonstrate step-by-step problem-solving processes |
+| [text-to-python.ipynb](./text-to-code/text-to-python.ipynb) | Text-to-Code        | Generate Python code from natural language instructions with validation and evaluation |
+| [text-to-python-evol.ipynb](./text-to-code/text-to-python-evol.ipynb) | Text-to-Code        | Build advanced Python code generation with evolutionary improvements and iterative refinement |
+| [text-to-sql.ipynb](./text-to-code/text-to-sql.ipynb) | Text-to-Code        | Create SQL queries from natural language descriptions with validation and testing |