AoA: tokenizer loading fallbacks for transformers-5.x-saved checkpoints - #5
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bylinina wants to merge 2 commits into
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AoA: tokenizer loading fallbacks for transformers-5.x-saved checkpoints#5bylinina wants to merge 2 commits into
bylinina wants to merge 2 commits into
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…eTrainedTokenizerFast), matching other pipeline call sites
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Models saved with transformers 5.x record tokenizer_class: TokenizersBackend, which transformers 4.51.3 (pinned by this pipeline) cannot resolve. The rest of the pipeline (sentence_zero_shot, reading, finetune) already handles this via a PreTrainedTokenizerFast fallback (merged in #3); the AoA module has two call sites that were missed:
evaluation_functions.load_tokenizer_for_step — the AutoProcessor load raises per checkpoint, the surprisal loop swallows it, and the run completes "successfully" with an empty results dir (no surprisal.json; AoA is silently set to None at collation).
run.py scoring step — a raw AutoTokenizer load crashes after surprisal extraction succeeds, so aoa_score.json is never written.
This PR applies the same 3-level fallback (AutoProcessor → AutoTokenizer → PreTrainedTokenizerFast) to both call sites, matching the existing pattern. Tested on DeBERTa MLM checkpoints saved with transformers 5.x: both failure modes reproduce on upstream main and are resolved with this patch (surprisal extraction across 19 chck_*M revisions + scoring complete). No behavior change for models whose tokenizers load via AutoProcessor.