---
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.