Guitar tablature transcription predicts the string and fret position for each note so that the resulting tablature reproduces the target musical part. Prior sequence-to-sequence approaches have shown promising results on large-scale datasets, but their generalization behavior across different dataset scales remains less explored. In this work, we propose a guitar tablature transcription framework with explicit note-event tokenization and regularized training. The proposed decoder token representation incorporates note-event tokens together with TAB tokens, allowing note boundaries, pitch-related events, and string-fret positions to be represented more explicitly. We evaluate the proposed framework on DadaGP, a large-scale dataset, and Francois Leduc, a small-scale dataset. Our method improves tablature accuracy over the Fretting Transformer baseline on DadaGP, with especially strong gains when trained directly on the small-scale Leduc dataset. We further introduce a pitch-validity constrained decoding strategy that masks pitch-invalid TAB candidates during generation rather than correcting them after decoding and simultaneously preserves the original timing and note structure from the input. This constraint improves tablature accuracy and provides a controlled setting for measuring how much error remains after pitch-invalid predictions are removed. Our code will be released at:https://github.com/MusicGuitarTab/GuitarTab

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