Which Datasets for RAG and fine tuning LLM?

Beginners
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I want to make a RAG + BERT model to advise on Indian legislation ,but I don’t know what kind of different dataset should I use for RAG and LLM?? Which dataset would be efficient for them?

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By Hugging Chat:

To create a Retrieval-Augmented Generation (RAG) + BERT model for advising on Indian legislation, follow these organized steps:

1. Data Collection

  • RAG Dataset:
    • Use the LEDGAR dataset from Hugging Face, which, while not specific to India, provides a strong foundation for legal documents. Supplement with the Indian Constitution, acts, and case laws. Consider sourcing from Indian government websites for specific regional legal documents.
  • LLM Dataset:
    • Combine datasets like English Gigaword and BookCorpus for a broad corpus. Enhance with Indian-specific content such as legal blogs, news articles, and government publications. Consider multilingual support if regional languages are needed.

2. Data Preprocessing

  • RAG: Preprocess legal documents into chunks with overlap. Ensure proper formatting and cleaning to enhance retrieval accuracy.
  • LLM: Tokenize text, remove duplicates, and split data into training and validation sets. Consider synthetic data generation to supplement underrepresented areas.

3. Model Selection and Fine-tuning

  • Use Legal-BERT for its legal context understanding. Consider ModernBERT for longer context handling. Fine-tune these models on your legal corpus.

4. Retrieval System Setup

  • Vector Database: Implement FAISS or ChromaDB for efficient document retrieval. Preprocess documents into embeddings using models like Legal-BERT or Doc Dale for better accuracy.

5. Integration and Validation

  • Integrate the retrieval and generation systems. Test the model with diverse queries to ensure relevance and accuracy. Validate the model’s performance, especially in handling complex legal queries.

6. Considerations

  • Language Support: Use multi-language BERT if handling regional languages.
  • Data Availability: Address potential scarcity by augmenting with synthetic data and sourcing from reputable Indian legal sites.

7. Implementation Steps

  • Outline and execute each step methodically, starting with data sourcing, preprocessing, model setup, and integration, ensuring thorough testing and validation throughout.

By following these steps, you can develop an effective RAG + BERT model tailored for advising on Indian legislation, leveraging available datasets and models while addressing specific regional needs.