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: semantictask,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:
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
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.