Data_Understanding

Data Understanding Document: LLMOps Python Package

1. Data Sources

Identify the source of data used for training, evaluation, or RAG.

• Primary Source: Synthetic datasets and public benchmarks (e.g., SQuAD).
• Format: JSONL / Parquet.
• Repository Location: Data folder

2. Dataset Description

• Size: ~10k samples per evaluation set.
• Key Features/Columns:
  • prompt: Input text for the LLM.
  • ground_truth: Expected output for evaluation.
  • context: Supporting information for RAG.

3. Data Quality Analysis (LLM Specific)

Note

For LLMs, focus on text quality, diversity, and potential biases.

• Completeness: 100% (No missing prompts).
• Text Cleanliness: High (Pre-processed via ruff-based sanity checks).
• Diversity/Bias: Diversified across 5 major industry domains.

4. Exploration Results

• Key Insights: Models show better performance on short contexts (< 2000 tokens).
• Visualizations: Notebooks

5. REPOSITORY CONTEXT

Important

Link to scripts used for data loading and analysis.

• Data Loaders: Loaders
• EDA Scripts: EDA

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