nlp

Use when choosing how to tokenize text or which transformer type fits an NLP task, when a tokenizer over-fragments non-English text or inflates token cost, when picking a language metric, or when classification, NER or summarization output looks wrong and it is unclear whether the tokenizer, the architecture or the metric is at fault. Covers subword tokenizers, encoder versus decoder versus encoder-decoder choice,…