Datasets:

carlahq
/

demo-tabular-benchmark-containers

number_of_rings float64 1 29	sex large_stringclasses 3 values	length_mm float64 0.08 0.82	diameter_mm float64 0.06 0.65	height_mm float64 0 1.13	whole_weight_g float64 0 2.83	shucked_weight_g float64 0 1.49	viscera_weight_g float64 0 0.76	shell_weight_g float64 0 1.01
15	M	0.455	0.365	0.095	0.514	0.2245	0.101	0.15
7	M	0.35	0.265	0.09	0.2255	0.0995	0.0485	0.07
9	F	0.53	0.42	0.135	0.677	0.2565	0.1415	0.21
10	M	0.44	0.365	0.125	0.516	0.2155	0.114	0.155
7	I	0.33	0.255	0.08	0.205	0.0895	0.0395	0.055
8	I	0.425	0.3	0.095	0.3515	0.141	0.0775	0.12
20	F	0.53	0.415	0.15	0.7775	0.237	0.1415	0.33
16	F	0.545	0.425	0.125	0.768	0.294	0.1495	0.26
9	M	0.475	0.37	0.125	0.5095	0.2165	0.1125	0.165
19	F	0.55	0.44	0.15	0.8945	0.3145	0.151	0.32
14	F	0.525	0.38	0.14	0.6065	0.194	0.1475	0.21
10	M	0.43	0.35	0.11	0.406	0.1675	0.081	0.135
11	M	0.49	0.38	0.135	0.5415	0.2175	0.095	0.19
10	F	0.535	0.405	0.145	0.6845	0.2725	0.171	0.205
10	F	0.47	0.355	0.1	0.4755	0.1675	0.0805	0.185
12	M	0.5	0.4	0.13	0.6645	0.258	0.133	0.24
7	I	0.355	0.28	0.085	0.2905	0.095	0.0395	0.115
10	F	0.44	0.34	0.1	0.451	0.188	0.087	0.13
7	M	0.365	0.295	0.08	0.2555	0.097	0.043	0.1
9	M	0.45	0.32	0.1	0.381	0.1705	0.075	0.115
11	M	0.355	0.28	0.095	0.2455	0.0955	0.062	0.075
10	I	0.38	0.275	0.1	0.2255	0.08	0.049	0.085
12	F	0.565	0.44	0.155	0.9395	0.4275	0.214	0.27
9	F	0.55	0.415	0.135	0.7635	0.318	0.21	0.2
10	F	0.615	0.48	0.165	1.1615	0.513	0.301	0.305
11	F	0.56	0.44	0.14	0.9285	0.3825	0.188	0.3
11	F	0.58	0.45	0.185	0.9955	0.3945	0.272	0.285
12	M	0.59	0.445	0.14	0.931	0.356	0.234	0.28
15	M	0.605	0.475	0.18	0.9365	0.394	0.219	0.295
11	M	0.575	0.425	0.14	0.8635	0.393	0.227	0.2
10	M	0.58	0.47	0.165	0.9975	0.3935	0.242	0.33
15	F	0.68	0.56	0.165	1.639	0.6055	0.2805	0.46
18	M	0.665	0.525	0.165	1.338	0.5515	0.3575	0.35
19	F	0.68	0.55	0.175	1.798	0.815	0.3925	0.455
13	F	0.705	0.55	0.2	1.7095	0.633	0.4115	0.49
8	M	0.465	0.355	0.105	0.4795	0.227	0.124	0.125
16	F	0.54	0.475	0.155	1.217	0.5305	0.3075	0.34
8	F	0.45	0.355	0.105	0.5225	0.237	0.1165	0.145
11	F	0.575	0.445	0.135	0.883	0.381	0.2035	0.26
9	M	0.355	0.29	0.09	0.3275	0.134	0.086	0.09
9	F	0.45	0.335	0.105	0.425	0.1865	0.091	0.115
14	F	0.55	0.425	0.135	0.8515	0.362	0.196	0.27
5	I	0.24	0.175	0.045	0.07	0.0315	0.0235	0.02
5	I	0.205	0.15	0.055	0.042	0.0255	0.015	0.012
4	I	0.21	0.15	0.05	0.042	0.0175	0.0125	0.015
7	I	0.39	0.295	0.095	0.203	0.0875	0.045	0.075
9	M	0.47	0.37	0.12	0.5795	0.293	0.227	0.14
7	F	0.46	0.375	0.12	0.4605	0.1775	0.11	0.15
6	I	0.325	0.245	0.07	0.161	0.0755	0.0255	0.045
9	F	0.525	0.425	0.16	0.8355	0.3545	0.2135	0.245
8	I	0.52	0.41	0.12	0.595	0.2385	0.111	0.19
7	M	0.4	0.32	0.095	0.303	0.1335	0.06	0.1
10	M	0.485	0.36	0.13	0.5415	0.2595	0.096	0.16
10	F	0.47	0.36	0.12	0.4775	0.2105	0.1055	0.15
7	M	0.405	0.31	0.1	0.385	0.173	0.0915	0.11
8	F	0.5	0.4	0.14	0.6615	0.2565	0.1755	0.22
8	M	0.445	0.35	0.12	0.4425	0.192	0.0955	0.135
8	M	0.47	0.385	0.135	0.5895	0.2765	0.12	0.17
4	I	0.245	0.19	0.06	0.086	0.042	0.014	0.025
7	F	0.505	0.4	0.125	0.583	0.246	0.13	0.175
7	M	0.45	0.345	0.105	0.4115	0.18	0.1125	0.135
9	M	0.505	0.405	0.11	0.625	0.305	0.16	0.175
10	F	0.53	0.41	0.13	0.6965	0.302	0.1935	0.2
7	M	0.425	0.325	0.095	0.3785	0.1705	0.08	0.1
8	M	0.52	0.4	0.12	0.58	0.234	0.1315	0.185
8	M	0.475	0.355	0.12	0.48	0.234	0.1015	0.135
12	F	0.565	0.44	0.16	0.915	0.354	0.1935	0.32
13	F	0.595	0.495	0.185	1.285	0.416	0.224	0.485
10	F	0.475	0.39	0.12	0.5305	0.2135	0.1155	0.17
6	I	0.31	0.235	0.07	0.151	0.063	0.0405	0.045
13	M	0.555	0.425	0.13	0.7665	0.264	0.168	0.275
8	F	0.4	0.32	0.11	0.353	0.1405	0.0985	0.1
20	F	0.595	0.475	0.17	1.247	0.48	0.225	0.425
11	M	0.57	0.48	0.175	1.185	0.474	0.261	0.38
13	F	0.605	0.45	0.195	1.098	0.481	0.2895	0.315
15	F	0.6	0.475	0.15	1.0075	0.4425	0.221	0.28
9	M	0.595	0.475	0.14	0.944	0.3625	0.189	0.315
10	F	0.6	0.47	0.15	0.922	0.363	0.194	0.305
11	F	0.555	0.425	0.14	0.788	0.282	0.1595	0.285
14	F	0.615	0.475	0.17	1.1025	0.4695	0.2355	0.345
9	F	0.575	0.445	0.14	0.941	0.3845	0.252	0.285
12	M	0.62	0.51	0.175	1.615	0.5105	0.192	0.675
16	F	0.52	0.425	0.165	0.9885	0.396	0.225	0.32
21	M	0.595	0.475	0.16	1.3175	0.408	0.234	0.58
14	M	0.58	0.45	0.14	1.013	0.38	0.216	0.36
12	F	0.57	0.465	0.18	1.295	0.339	0.2225	0.44
13	M	0.625	0.465	0.14	1.195	0.4825	0.205	0.4
10	M	0.56	0.44	0.16	0.8645	0.3305	0.2075	0.26
9	F	0.46	0.355	0.13	0.517	0.2205	0.114	0.165
12	F	0.575	0.45	0.16	0.9775	0.3135	0.231	0.33
15	M	0.565	0.425	0.135	0.8115	0.341	0.1675	0.255
12	M	0.555	0.44	0.15	0.755	0.307	0.1525	0.26
13	M	0.595	0.465	0.175	1.115	0.4015	0.254	0.39
10	F	0.625	0.495	0.165	1.262	0.507	0.318	0.39
15	M	0.695	0.56	0.19	1.494	0.588	0.3425	0.485
14	M	0.665	0.535	0.195	1.606	0.5755	0.388	0.48
9	M	0.535	0.435	0.15	0.725	0.269	0.1385	0.25
8	M	0.47	0.375	0.13	0.523	0.214	0.132	0.145
7	M	0.47	0.37	0.13	0.5225	0.201	0.133	0.165
10	F	0.475	0.375	0.125	0.5785	0.2775	0.085	0.155

End of preview. Expand in Data Studio

📦 Carla HQ — Tabular Benchmark CuratedContainers

Centralized repository of Data Foundry CuratedContainers curated for Carla HQ, TabICLv2, and the next generation of Tabular Foundation Models (TabPFN, EXAONE, Google TabFM).

Each container directory provides:

Columnar Parquet Data (dataset.parquet): Clean, type-normalized, and validated tabular dataset binary.
Standardized Task Molds (task_metadata.predictive-ml-task-mold-v1.json): Problem definitions, target attributes, and evaluation metrics.
Pre-computed Split Matrices (experiment_metadata.predictive-ml-splits-mold-v1.json): Repeated IID stratified train/test partitions ($10\times3$, $20\times3$, $3\times3$, seed 4267) and non-IID grouped/temporal splits.
Rich Provenance Metadata (dataset_metadata.dataset-mold-v1.json): Academic bibtex, NACE industry taxonomy, licenses, and curation logs.

📋 Available Tabular Benchmark Containers (21 Datasets)

Slug / Container	Dataset Name	Domain	Task Type	Target Column	Rows	Features	Split Regime	Container UUID
`abalone`	Abalone	social science	`regression`	`number_of_rings`	4,177	8	3x3 IID	`01a05c1d...`
`adult`	Adult	social science	`binary_classification`	`income_bracket`	48,842	14	3x3 IID	`01a05c1d...`
`airfoil_self_noise`	Airfoil Self Noise	social science	`regression`	`scaled-sound-pressure`	1,503	5	10x3 IID	`01a05c1d...`
`amazon_employee_access`	Amazon Employee Access	social science	`binary_classification`	`access_approved`	32,769	9	3x3 IID	`01a05c1d...`
`bank_marketing`	Bank Marketing	finance	`binary_classification`	`term_deposit_subscribed`	45,211	15	3x3 IID	`01a05c1d...`
`blood_transfusion_service_center`	Blood Transfusion Service Center	medical & healthcare	`binary_classification`	`class`	748	4	20x3 IID	`01a05c1e...`
`breast_w`	Breast W	medical & healthcare	`binary_classification`	`diagnosis_class`	699	9	20x3 IID	`01a05c1d...`
`car`	Car	social science	`multiclass_classification`	`car_acceptability`	1,728	6	10x3 IID	`01a05c1d...`
`compas_two_years`	Compas Two Years	social science	`binary_classification`	`two_year_recidivism`	5,278	13	3x3 IID	`01a05c1d...`
`credit_g`	Credit G	finance	`binary_classification`	`credit_risk_classification`	1,000	20	10x3 IID	`01a05c1d...`
`diabetes`	Diabetes	social science	`binary_classification`	`diabetes_diagnosis`	768	8	10x3 IID	`01a05c1d...`
`employee_salaries`	Employee Salaries	social science	`regression`	`current_annual_salary_usd`	9,228	10	3x3 IID	`01a05c1d...`
`fitness_club`	Fitness Club	social science	`binary_classification`	`attended`	1,500	6	10x3 IID	`01a05c30...`
`house_sales`	House Sales	social science	`regression`	`sale_price_usd`	21,613	20	3x3 IID	`01a05c1d...`
`houses`	Houses	social science	`regression`	`median_house_value_usd`	20,640	8	3x3 IID	`01a05c1d...`
`monks_problems_2`	Monks Problems 2	social science	`binary_classification`	`is_target_concept_met`	601	6	20x3 IID	`01a05c1d...`
`phoneme`	Phoneme	social science	`binary_classification`	`phoneme_sound_class`	5,404	5	3x3 IID	`01a05c1d...`
`spambase`	Spambase	social science	`binary_classification`	`is_spam`	4,601	57	3x3 IID	`01a05c1d...`
`telco_customer_churn`	Telco Customer Churn	social science	`binary_classification`	`churn_status`	7,043	20	3x3 IID	`01a05c1d...`
`titanic`	Titanic	social science	`binary_classification`	`survived`	1,309	11	10x3 IID	`01a05c1d...`
`vehicle`	Vehicle	social science	`multiclass_classification`	`vehicle_type`	846	18	10x3 IID	`01a05c1d...`

🚀 Quickstart & Usage

1. Python (`data-foundry` Integration)

from data_foundry.curation_container import CuratedContainer
from huggingface_hub import snapshot_download

# Download container from Hugging Face Hub
container_dir = snapshot_download(
    repo_id="carlahq/demo-tabular-benchmark-containers",
    repo_type="dataset",
    allow_patterns=["containers/credit_g/**"],
)

# Load CuratedContainer directly
container = CuratedContainer.load(container_dir + "/containers/credit_g/<uuid>")
print(container.dataset.head())
print("Target column:", container.task_metadata.target_column_name)
print("Evaluation splits:", container.experiment_metadata.splits)

2. Download via `huggingface-cli`

# Download all CuratedContainers into local warehouse directory
huggingface-cli download carlahq/demo-tabular-benchmark-containers --repo-type dataset --include "containers/**" --local-dir ./benchmarks/data/

# Download a single dataset container (e.g. fitness_club)
huggingface-cli download carlahq/demo-tabular-benchmark-containers --repo-type dataset --include "containers/fitness_club/**" --local-dir ./benchmarks/data/

3. Automated Sync with `nanotabicl`

# Download and synchronize all containers into benchmarks/data/containers/
python benchmarks/curation/sync_hf_containers.py --download

# Benchmark directly using WebGPU or PyTorch runners
pnpm benchmark credit-g --mode streaming
python benchmarks/runners/benchmark_demo_dataset.py credit-g --mode base --device mps

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📦 Carla HQ — Tabular Benchmark CuratedContainers

📋 Available Tabular Benchmark Containers (21 Datasets)

🚀 Quickstart & Usage

1. Python (data-foundry Integration)

2. Download via huggingface-cli

3. Automated Sync with nanotabicl

1. Python (`data-foundry` Integration)

2. Download via `huggingface-cli`

3. Automated Sync with `nanotabicl`