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You are preparing raw text so it can be used in NLP model training. The data comes from real user content, so it includes inconsistent casing, HTML fragments, duplicated records, long documents, and domain specific terms that standard preprocessing may not handle well. You need a pipeline that is efficient enough for repeated training runs and clean enough to support downstream transformer fine-tuning.
How would you process text data efficiently for AI training tasks?
