Identifying a Good Dataset

When fine-tuning a large language model (LLM) with SeekrFlow, selecting a good dataset is critical for achieving optimal performance. Here are the key factors to consider when identifying and preparing a suitable dataset:

Data Relevance

Factors to Consider:

Domain Specificity: Ensure the dataset is relevant to the domain of the use case. The data should reflect the context in which the model will be applied.

Content Appropriateness: The data should be appropriate for the specific tasks the model will perform. For instance, if the model is for medical compliance, the dataset should include medical texts.

Example:

For a financial compliance model, use datasets comprising financial reports, regulatory filings, and compliance documents.

Data Quality

Factors to Consider:

Accuracy: The dataset should be free of errors and accurately reflect the information needed.

Consistency: Ensure consistent formatting and structure throughout the dataset.

Completeness: The dataset should be comprehensive, covering all necessary aspects of the task.

Example:

Use validated and peer-reviewed medical journals for a healthcare-related model to ensure high data accuracy and reliability.

Data Volume

Factors to Consider:

Sufficient Size: The dataset should be large enough to train the model effectively, but SeekrFlow can work efficiently with minimal data due to its principle alignment feature.

Balanced Representation: Ensure the dataset has a balanced representation of all categories and scenarios relevant to the use case.

Example:

For a customer service chatbot, include a wide range of customer queries and responses to cover various scenarios.

Data Format

Factors to Consider:

Supported Formats: SeekrFlow supports ‘parquet’ and JSON lines (.jsonl)’ formats.

Data Diversity

Factors to Consider:

Variety of Sources: Use data from diverse sources to ensure the model is robust and can handle different inputs.

Multiple Perspectives: Incorporate data that provides multiple perspectives on the subject matter to avoid bias.

Example:

For a news summarization model, use articles from various news outlets to capture different writing styles and viewpoints.

Data Annotations

Factors to Consider:

Labeled Data: Use annotated data where possible, as it helps in supervised learning tasks.

Quality of Annotations: Ensure the annotations are accurate and consistent.

Example:

Use labeled datasets for sentiment analysis, where each text entry is tagged with its corresponding sentiment.

Compliance and Ethical Considerations

Factors to Consider:

Data Privacy: Ensure the dataset complies with data privacy regulations (e.g. GDPR).

Ethical Use: Avoid data that could introduce bias or unethical outcomes.

Example:

For a healthcare application, anonymize patient data to protect privacy and comply with HIPAA regulations.

Uploading Data

Use SeekrFlow’s API (https://docs.seekr.com/reference/put_flow-files) to upload the processed dataset.