Part 6/11:
To address these issues, the developer explored various approaches, including using libraries like SpaCy and NLTK for de-duplication. Eventually, regex-based methods proved most effective for correcting OCR artifacts, cleaning the dataset for smoother downstream processing.
Chunk Management and Token Optimization
In preparing data for GPT-3, balancing chunk size was crucial because:
Larger chunks exceed token limits, increasing the risk of incomplete processing.
Smaller chunks are easier to analyze but require more management.