Jianshu001/arabic-daily-batch01-cascade-86
Batch 01 — Cascade 86 86 records (of a 100-record cascade run) after dropping 14 records that had <|channel>thought markers leak into the thinking field (caused by truncated Gemma rewriter output when max_tokens ran out mid-thinking). Pipeline Cascade-regenerate from the first detected issue (user assistant-greet, assistant AI self-ref, or user sycophant/summary) to end of conversation. Run Gemma-as-rewriter on every assistant thinking and on any text containing… See the full description on the dataset page: https://huggingface.co/datasets/Jianshu001/arabic-daily-batch01-cascade-86.
Batch 01 — Cascade 86
86 records (of a 100-record cascade run) after dropping 14 records that had <|channel>thought markers leak into the thinking field (caused by truncated Gemma rewriter output when max_tokens ran out mid-thinking).
Pipeline
- Cascade-regenerate from the first detected issue (user assistant-greet, assistant AI self-ref, or user sycophant/summary) to end of conversation.
- Run Gemma-as-rewriter on every assistant thinking and on any text containing AI self-reference. The rewriter's meta-thinking gets absorbed into Gemma's discarded thinking trace; its answer is the clean output.
- Strip emojis from user messages; remove all audit fields.
Schema
- User message fields: turn, role, text
- Assistant message fields: turn, role, thinking, text
No audit fields — textoriginal, thinkingoriginal, rewritelog, and thinkingoriginallang are all stripped.
Known limitations
- 14/100 records dropped due to a bug where the rewriter's Gemma call was truncated before producing its answer; the fix (6000 max_tokens + truncation guards) lands in the next batch.
- Pre-cascade cleanup pass means most non-cascade assistant turns were also run through the rewriter, greatly reducing system-prompt echo vs prior batches.