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Datasets:
mireklzicar
/
cellarc_100k_meta

Tasks:
Other
Text Generation
Visual Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
language-modeling
visual-question-answering
image-inpainting
Languages:
English
Size:
10K - 100K
Tags:
cellular-automata
arc
reasoning
Libraries:
Datasets
Dask
License:
Dataset card Data Studio Files
xet
Community
2
Miroslav Lžičař commited on
Commit
f5d98d6
·
1 Parent(s): 3ef150b

Replace dataset with new build

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Files changed (50) hide show
  1. README.md +83 -84
  2. data/test/rho10/test_rho10.jsonl +0 -0
  3. data/test/rho10/test_rho10_manifest.json +0 -81
  4. data/test/rho10/test_rho10_meta.jsonl +0 -0
  5. data/test/rho10/test_rho10_seen_fingerprints.txt +0 -200
  6. data/test/rho20/test_rho20.jsonl +0 -0
  7. data/test/rho20/test_rho20_manifest.json +0 -81
  8. data/test/rho20/test_rho20_meta.jsonl +0 -0
  9. data/test/rho20/test_rho20_seen_fingerprints.txt +0 -200
  10. data/test/rho40/test_rho40.jsonl +0 -0
  11. data/test/rho40/test_rho40_manifest.json +0 -81
  12. data/test/rho40/test_rho40_meta.jsonl +0 -0
  13. data/test/rho40/test_rho40_seen_fingerprints.txt +0 -200
  14. data/test/rho5/test_rho5.jsonl +0 -0
  15. data/test/rho5/test_rho5_manifest.json +0 -81
  16. data/test/rho5/test_rho5_meta.jsonl +0 -0
  17. data/test/rho5/test_rho5_seen_fingerprints.txt +0 -200
  18. data/test/rho80/test_rho80.jsonl +0 -0
  19. data/test/rho80/test_rho80.parquet +0 -3
  20. data/test/rho80/test_rho80_manifest.json +0 -81
  21. data/test/rho80/test_rho80_meta.jsonl +0 -0
  22. data/test/rho80/test_rho80_seen_fingerprints.txt +0 -200
  23. data/{test/rho5/test_rho5.parquet → test_extrapolation.jsonl} +2 -2
  24. data/{test/rho20/test_rho20.parquet → test_extrapolation.parquet} +2 -2
  25. data/test_interpolation.jsonl +3 -0
  26. data/{test/rho10/test_rho10.parquet → test_interpolation.parquet} +2 -2
  27. data/train.jsonl +3 -0
  28. data/{train/train.parquet → train.parquet} +2 -2
  29. data/train/jsonl/train_part000.jsonl +0 -0
  30. data/train/jsonl/train_part001.jsonl +0 -0
  31. data/train/jsonl/train_part002.jsonl +0 -0
  32. data/train/jsonl/train_part003.jsonl +0 -0
  33. data/train/jsonl/train_part004.jsonl +0 -0
  34. data/train/jsonl/train_part005.jsonl +0 -0
  35. data/train/jsonl/train_part006.jsonl +0 -0
  36. data/train/jsonl/train_part007.jsonl +0 -0
  37. data/train/jsonl/train_part008.jsonl +0 -0
  38. data/train/jsonl/train_part009.jsonl +0 -0
  39. data/train/meta/train_part000_meta.jsonl +0 -3
  40. data/train/meta/train_part001_meta.jsonl +0 -3
  41. data/train/meta/train_part002_meta.jsonl +0 -3
  42. data/train/meta/train_part003_meta.jsonl +0 -3
  43. data/train/meta/train_part004_meta.jsonl +0 -3
  44. data/train/meta/train_part005_meta.jsonl +0 -3
  45. data/train/meta/train_part006_meta.jsonl +0 -3
  46. data/train/meta/train_part007_meta.jsonl +0 -3
  47. data/train/meta/train_part008_meta.jsonl +0 -3
  48. data/train/meta/train_part009_meta.jsonl +0 -3
  49. data/train/train_manifest.json +0 -167
  50. data/train/train_seen_fingerprints.txt +0 -3
README.md CHANGED
@@ -1,96 +1,95 @@
1
- ---
2
- language:
3
- - en
4
- pretty_name: CellARC-100K Meta
5
- license: cc-by-4.0
6
- annotations_creators:
7
- - machine-generated
8
- task_categories:
9
- - other
10
- - image-to-image
11
- - visual-question-answering
12
- - zero-shot-classification
13
- tags:
14
- - cellular-automata
15
- - few-shot
16
- - reasoning
17
- - program-synthesis
18
- dataset_info:
19
- features:
20
- - name: fingerprint
21
- type: string
22
- - name: meta
23
- type: json
24
- - name: rule_table
25
- type: json
26
- size_in_bytes: 226504780
27
- splits:
28
- train: 100,000
29
- validation: 1,000
30
- test: 1,000
31
- ---
32
-
33
- # CellARC-100K Meta
34
-
35
- The meta release extends the core dataset with detailed episode metadata, rule-table exports, and
36
- auxiliary statistics suitable for analysis and simulator reconstruction.
37
-
38
- ## Coverage Stratification
39
-
40
- | Partition | Coverage ρ | Episodes |
41
- | --- | --- | --- |
42
- | train | mixed ρ (0.00–0.50) | 100,000 |
43
- | validation | ρ = 0.10 | 200 |
44
- | validation | ρ = 0.20 | 200 |
45
- | validation | ρ = 0.40 | 200 |
46
- | validation | ρ = 0.05 | 200 |
47
- | validation | ρ = 0.80 | 200 |
48
- | test | ρ = 0.10 | 200 |
49
- | test | ρ = 0.20 | 200 |
50
- | test | ρ = 0.40 | 200 |
51
- | test | ρ = 0.05 | 200 |
52
- | test | ρ = 0.80 | 200 |
53
-
54
- ## Additional Files
55
-
56
- - ``data/*/meta/`` — per-shard metadata JSONL with morphology, coverage, and fingerprints.
57
- - ``metadata/dataset_stats.json`` — manifest snapshot after packaging.
58
- - ``metadata/rule_table_schema.json`` — documentation for the rule-table payload.
59
- - ``*_seen_fingerprints.txt`` — split-wise fingerprints for deduplication.
60
-
61
- ## Rule Table Format
62
-
63
- Each metadata record stores ``rule_table`` with lexicographically ordered outputs for all
64
- neighbourhoods of width ``2 * radius + 1``. The helper below converts the table into a callable
65
- suitable for ``cellpylib``:
66
 
67
- ```python
68
- def make_rule(rule_payload):
69
- k = rule_payload["alphabet_size"]
70
- values = rule_payload["values"]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
71
 
72
- def apply(neighbourhood, *_):
73
- idx = 0
74
- for cell in neighbourhood:
75
- idx = idx * k + cell
76
- return values[idx]
77
 
78
- return apply
 
 
 
 
 
 
 
 
 
 
 
 
79
  ```
80
 
81
- To load the metadata shards:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
82
 
83
  ```python
84
  from datasets import load_dataset
85
 
86
- meta = load_dataset(
87
- "mireklzicar/cellarc_100k_meta",
88
- data_files={"train": "data/train/meta/train_part*_meta.jsonl"},
89
- split="train",
90
- )
 
 
 
 
91
  ```
92
 
93
- ## Licensing
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
94
 
95
- - Dataset: Creative Commons Attribution 4.0 International (CC BY 4.0)
96
- - Code: MIT License
 
1
+ # cellarc_100k_meta
2
+ CellARc 100k Meta is the metadata-rich companion to the lightweight CellARc split, retaining every field emitted during generation while staying Hugging Face friendly.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3
 
4
+ ## Data quick facts
5
+ - Vocabulary: cell values use integer labels 0–5 (alphabet size up to 6).
6
+ - Max list length: every `train`, `query`, or `solution` list has at most 21 entries (median 13).
7
+ - Flattened full task: concatenating all lists in an episode yields at most 252 integers (median 156).
8
+ - JSON keys: `id`, `train[].input`, `train[].output`, `query`, `solution`, `meta`, `rule_table`.
9
+
10
+ ### Episode schema sample
11
+ ```
12
+ {
13
+ "id": "…",
14
+ "train": [
15
+ {"input": [0, 1, …], "output": [2, 0, …]}, # each list length ≤ 21
16
+ …
17
+ ],
18
+ "query": [3, 0, …], # length ≤ 21
19
+ "solution": [4, 5, …], # length ≤ 21
20
+ "meta": { … }, # full provenance + analytics
21
+ "rule_table": { … } # base64-encoded CA lookup
22
+ }
23
+ ```
24
+
25
+ ## Dataset details
26
+
27
+ `cellarc_100k_meta` augments the lightweight `cellarc_100k` release with the complete per-episode metadata that was captured during dataset generation. The Parquet assets (`data/*.parquet`) are identical between both packages so models can be trained interchangeably; the distinction lives in the JSONL representation:
28
 
29
+ - `cellarc_100k/data/*.jsonl` → minimal supervision fields (`id`, `train`, `query`, `solution`).
30
+ - `cellarc_100k_meta/data/*.jsonl` → full records containing the metadata, coverage diagnostics and rule tables used to produce each episode.
 
 
 
31
 
32
+ ## Contents
33
+
34
+ ```
35
+ cellarc_100k_meta/
36
+ ├── data/
37
+ │ ├── train.{jsonl,parquet}
38
+ │ ├── val.{jsonl,parquet}
39
+ │ ├── test_interpolation.{jsonl,parquet}
40
+ │ └── test_extrapolation.{jsonl,parquet}
41
+ ├── data_files.json
42
+ ├── dataset_stats.json
43
+ ├── features.json # identical to the lightweight package
44
+ └── README.md # this file
45
  ```
46
 
47
+ Because the JSONL retains the full metadata, the files are substantially larger (≈4.6 × the lightweight versions) but remain line-delimited for streaming.
48
+
49
+ ## Additional fields
50
+
51
+ Each JSON line mirrors the original generation payload:
52
+
53
+ - `meta`: structured description of the episode (`alphabet_size`, `radius`, `steps`, `window`, train/query span descriptors, construction, family, Langton λ statistics, entropy bins, coverage metrics, morphology signals, seeds, wrap flags, etc.).
54
+ - `rule_table`: explicit lookup table for the cellular automaton rule encoded as base64 (matching the copy nested inside `meta`).
55
+ - `id`: injected fingerprint alias for convenience (`meta["fingerprint"]`).
56
+
57
+ The nested objects (e.g. `coverage`, `morphology`, `train_spans`) remain untouched so downstream tooling can parse them without modification.
58
+
59
+ For the canonical supervision tensors (`train`, `query`, `solution`) refer to the [cellarc_100k README](../cellarc_100k/README.md).
60
+
61
+ ## Working with the metadata
62
 
63
  ```python
64
  from datasets import load_dataset
65
 
66
+ data_files = {
67
+ split: f"artifacts/datasets/cellarc_100k_meta/data/{split}.jsonl"
68
+ for split in ("train", "val", "test_interpolation", "test_extrapolation")
69
+ }
70
+ full = load_dataset("json", data_files=data_files)
71
+
72
+ episode = full["train"][0]
73
+ print(episode["id"], episode["meta"]["lambda"], episode["coverage"]["fraction"])
74
+ print(len(episode["meta"]["train_spans"]), "train windows, context =", episode["meta"]["train_context"])
75
  ```
76
 
77
+ Use cases for the Meta package include:
78
+
79
+ 1. Analyzing curriculum difficulty via λ/entropy bins or morphology descriptors.
80
+ 2. Filtering subsets by construction (`cycle`, `unrolled`, `hybrid`) or family (`random`, `totalistic`, …).
81
+ 3. Reconstructing the exact CA rule during evaluation (via `rule_table`).
82
+
83
+ ## Shared statistics
84
+
85
+ `dataset_stats.json`, `features.json` and the Parquet files are byte-for-byte identical to those shipped with `cellarc_100k`, ensuring consistent training/evaluation splits and schema definitions across both packages.
86
+
87
+ ## Reproduction & license
88
+
89
+ Generated via:
90
+
91
+ ```bash
92
+ python scripts/build_hf_dataset.py --overwrite
93
+ ```
94
 
95
+ License: inherits the repository’s license (specify here if a dedicated dataset license is selected). Please cite the CellARc project when using this dataset.
 
data/test/rho10/test_rho10.jsonl DELETED
The diff for this file is too large to render. See raw diff
 
data/test/rho10/test_rho10_manifest.json DELETED
@@ -1,81 +0,0 @@
1
- {
2
- "split": "test_rho10",
3
- "count": 200,
4
- "lambda_bins": {
5
- "ordered": 66,
6
- "edge": 67,
7
- "chaotic": 67
8
- },
9
- "stats": {
10
- "count": 200,
11
- "lambda": {
12
- "mean": 0.40185489583333356,
13
- "min": 0.0078125,
14
- "max": 0.9814814814814815
15
- },
16
- "coverage_fraction": {
17
- "mean": 1.0,
18
- "min": 1.0,
19
- "max": 1.0
20
- },
21
- "coverage_windows": {
22
- "mean": 97.725,
23
- "min": 27.0,
24
- "max": 216.0
25
- },
26
- "families": {
27
- "random": 87,
28
- "outer_totalistic": 44,
29
- "linear_mod_k": 9,
30
- "threshold": 13,
31
- "totalistic": 20,
32
- "outer_inner_totalistic": 27
33
- },
34
- "coverage_modes": {
35
- "full_cycle_partition": 200
36
- }
37
- },
38
- "schema_version": "1.0.0",
39
- "dataset_version": "dev",
40
- "generator": {
41
- "kwargs": {
42
- "unique_by": "tstep",
43
- "balance_by": "lambda",
44
- "coverage_fraction": 0.1,
45
- "coverage_mode": "chunked",
46
- "target_avg_train_len": 48,
47
- "compute_complexity": true,
48
- "annotate_morphology": true,
49
- "query_within_coverage": true,
50
- "schema_version": "1.0.0",
51
- "dataset_version": "dev"
52
- },
53
- "base_seed": 5589,
54
- "shard_size": null
55
- },
56
- "shards": [
57
- {
58
- "path": "data/test/rho10/test_rho10.jsonl",
59
- "sha256": "4bce13812e46b0f347179702a158c12f9799aacd83d298939f81a2be629c0f11",
60
- "count": 200,
61
- "meta_path": "data/test/rho10/test_rho10_meta.jsonl",
62
- "meta_sha256": "f673b0e192949c81b89395d0440288429aa013fdab0d10cff4df23763f413179"
63
- }
64
- ],
65
- "artifacts": {
66
- "jsonl": [
67
- "data/test/rho10/test_rho10.jsonl"
68
- ],
69
- "meta_jsonl": [
70
- "data/test/rho10/test_rho10_meta.jsonl"
71
- ],
72
- "parquet": "data/test/rho10/test_rho10.parquet",
73
- "parquet_files": [
74
- "data/test/rho10/test_rho10.parquet"
75
- ],
76
- "parquet_sha256": {
77
- "data/test/rho10/test_rho10.parquet": "0637a50301e595b0e274171cf6f1fd739b4b117ca225cbd4331b5e8b20f52673"
78
- }
79
- },
80
- "fingerprints_file": "data/test/rho10/test_rho10_seen_fingerprints.txt"
81
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
data/test/rho10/test_rho10_meta.jsonl DELETED
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data/test/rho10/test_rho10_seen_fingerprints.txt DELETED
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