AniDB is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
v 2022-04, © 2002-2022 by AniDB; all rights reserved. [0s] - 08.03.2026 22:22:47
If you are working with the dataset and trying to load it using a RoBERTa-based tokenizer or model wrapper, you have likely encountered the dreaded configuration mismatch error, often referenced in tracker logs as "sets 136zip fix" .
The refers to a corrective update applied to natural language processing (NLP) models within the WALS (Wordpieces and Language Structures) framework, specifically targeting the RoBERTa architecture. This update addresses a critical data handling anomaly—often referred to as the "136-zip" error—where specific input sets caused tokenization misalignments or vocabulary indexing failures during inference or training. The fix ensures robust handling of compressed data structures and stabilizes the model's performance on downstream tasks involving complex token sets. wals roberta sets 136zip fix
Standard unzippers fail on partial archives. 7-Zip has a "keep broken files" option: If you are working with the dataset and
AniDB is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
v 2022-04, © 2002-2022 by AniDB; all rights reserved. [0s] - 08.03.2026 22:22:47
