| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| task_categories: |
| - text-generation |
| - question-answering |
| tags: |
| - sft |
| - conversational |
| - reasoning |
| - variableby2d |
| - assistant-only-loss |
| pretty_name: Complexity Atlas Posttrain — Card Corpus V2 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| - split: validation |
| path: data/validation-*.parquet |
| - split: test |
| path: data/test-*.parquet |
| --- |
| |
| # Complexity Atlas Posttrain — Card Corpus V2 |
|
|
| An English supervised fine-tuning corpus generated from authored semantic |
| frames, role-separated prompt/answer/thinking plans, compatibility graphs, and |
| `VariableBy2D` reservoirs. All 15 task families, including natural dialogue, |
| belong to one audited corpus and one tokenizer-compatible training view. |
|
|
| ## Release |
|
|
| | Split | Examples | |
| |---|---:| |
| | Train | 224,654 | |
| | Validation | 2,478 | |
| | Test | 1,894 | |
| | **Total** | **229,026** | |
|
|
| The generator renders every registered valid scenario combination. No global |
| sampling quota, per-family truncation, or 400K cap is applied. |
|
|
| | Family | Examples | |
| |---|---:| |
| | `brainstorming_creativity` | 384 | |
| | `casual_conversation` | 52,794 | |
| | `context_clarification` | 9,216 | |
| | `conversation_empathy` | 2,048 | |
| | `critique_revision` | 64 | |
| | `explanation_learning` | 36,040 | |
| | `extraction_classification` | 3,456 | |
| | `grounded_qa` | 4,608 | |
| | `planning_comparison` | 384 | |
| | `practical_action` | 384 | |
| | `reasoning_verification` | 108,000 | |
| | `safety_uncertainty` | 3,072 | |
| | `summarization_synthesis` | 4,096 | |
| | `troubleshooting` | 384 | |
| | `writing_transformation` | 4,096 | |
|
|
| ## Behavioral coverage |
|
|
| The release contract requires learnable support—not merely one public anchor— |
| for the behaviors used during model promotion. |
|
|
| | Capability | Training examples | Domain count | |
| |---|---:|---:| |
| | Direct safety | 3,077 | 16 | |
| | Small arithmetic | 16,101 | 4 | |
| | Summarization | 4,096 | 8 | |
| | Writing transformation | 4,096 | 8 | |
| | Multi-constraint following | 4,000 | 4 | |
| | Concept definitions | 1,000 | 1 | |
| | General facts | 1,024 | 1 | |
| | Reflective conversation | 2,048 | 10 | |
| | Neutral greetings | 1,100 | 1 | |
|
|
| The casual family also contains 11,000 history-dependent multi-turn examples. |
| Earlier assistant turns are masked context; only the final assistant response is |
| supervised. |
|
|
| ## Certification |
|
|
| This artifact passed the full V2 release contract: |
|
|
| - behavior, capability coverage, integrity, distribution, composition, |
| near-duplicate, response-length, and split-leakage gates; |
| - tokenizer round-trip, final-assistant-only loss masking, and think/final |
| marker checks against the project 32K tokenizer; |
| - all 15 family roadmaps marked `PASS`; |
| - no exact or normalized composition leakage between train, validation, and |
| test. |
|
|
| The final-response length distribution in the training split is 76.05% direct |
| (1–25 words), 13.20% concise (26–80), 5.60% detailed (81–200), and 5.16% |
| extended (201–512). |
|
|
| Machine-readable evidence is included in `metadata/manifest.json`, |
| `metadata/audit.json`, and `metadata/roadmap.json`. |
|
|
| ## Tokenized 32K training shards |
|
|
| Framework-ready shards are published under `tokenized/32k-v2/`. |
|
|
| | Partition | Examples | Tokens | Supervised assistant tokens | |
| |---|---:|---:|---:| |
| | `train` | 224,654 | 28,944,057 | 18,538,127 | |
| | `eval` | 2,478 | 319,114 | 212,661 | |
| | `test` | 1,894 | 303,419 | 210,617 | |
| | **Total** | **229,026** | **29,566,590** | **18,961,405** | |
|
|
| Each partition contains: |
|
|
| - `input_ids.bin`: little-endian unsigned 32-bit token IDs; |
| - `labels.bin`: little-endian signed 32-bit causal labels; |
| - `examples.jsonl`: row boundaries and provenance; |
| - `loss_metadata.jsonl`: semantic `task`, `domain`, and two-dimensional loss |
| cell metadata for every example; |
| - `sft.idx.json`: hashes, dtypes, counts, masking contract, and tokenizer ID. |
|
|
| All context positions use the `-100` ignore index. Only the final assistant |
| tokens and EOS are supervised. The vocabulary size is 32,000, the chat |
| contract is `complexity-chat-v2`, and the tokenizer SHA-256 is |
| `852759014538299ed8e941a83fa1f254fbbaff7824189a4c7455f038cfede3f2`. |
|
|
| ## Two-dimensional full-shard weighting |
|
|
| The sidecars do not resample the dataset. Every row remains visible once per |
| epoch. During training, the framework resolves each example to a behavioral |
| group and a `task × domain` cell, then computes: |
|
|
| ```text |
| global_target(cell) = group_target(group) × cell_target(cell | group) |
| loss_weight(cell) = global_target(cell) / raw_visible_token_share(cell) |
| ``` |
|
|
| Weighted cross-entropy is normalized by visible weighted-token mass. This |
| balances gradient contribution while preserving the complete shard, natural |
| row frequency, deterministic split, and semantic diversity. Coefficients above |
| the configured safety limit are rejected rather than silently applied. |
|
|
| ## Source and license |
|
|
| Generated by the open-source |
| [`Complexity-ML/complexity-card-corpus`](https://github.com/Complexity-ML/complexity-card-corpus) |
| V2 pipeline at commit `6aaf71c`. |
|
|
| The dataset is released under **CC BY-NC 4.0**. Review the license before |
| training or redistributing a model, especially for commercial use. |
|
|
