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Premove ITN separates inverse text normalization into three steps:

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Generate

Deterministic Rust realizers enumerate structurally valid written forms for spans in the transcript. The model cannot invent an output that the candidate layer did not generate.

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Score

DeBERTa encodes the complete sentence once. It gives each candidate a score based on the source span, proposed replacement, candidate kinds, and sentence context.
Both replacements are generated for this span. The retained v0.1.0 prediction for the room code is one oh five is the room code is 105. This example does not imply that every ambiguity is resolved correctly; see the benchmark limitations.

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Decode

Candidates can overlap. Exact dynamic programming selects the highest-scoring compatible set of edits across the complete sentence. Leaving source text unchanged is always available through a zero-score KEEP path. The rules decide what can be written. The model decides what fits the context. The decoder decides which edits can coexist.

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What this design does not do

The scorer cannot generate arbitrary new strings. An unsupported format needs a Rust candidate before the model can select it. The decoder can also keep the source text when no candidate wins. This avoids forcing a rewrite for every number-like phrase, but it means a supported phrase is not guaranteed to change in every context. Read the implementation architecture for tensor, alignment, scoring, and decoding details. See supported forms for the current public boundary.