weighted_sampler

Perform weighted random sampling on a JSONL file by specifying a numeric weight field and the desired sample size to extract prioritized records. The process utilizes Python's random library to read your JSONL data and output a subset based on your custom weights.

Yes, you can sample JSONL records with both with-replacement and without-replacement modes. This allows you to either allow the same record to be selected multiple times or ensure each record appears only once in your sampled output.

The best way to prioritize specific data records during sampling is to use a numeric weight field to define their importance. This weighted sampling approach ensures that records with higher priority scores have a greater probability of being selected in the final subset.

Yes, weighted sampling works effectively for data prioritization and importance ranking tasks. By assigning higher numeric weights to critical JSONL records, the sampling mechanism naturally prioritizes them, yielding a dataset that reflects your defined importance scores.

You specify the sample size as a numeric parameter when initiating the weighted sampling process on your JSONL dataset. This defines the exact number of records to be extracted and saved to your output file based on the specified weights.