| --- |
| language: |
| - en |
| license: apache-2.0 |
| task_categories: |
| - tabular-classification |
| - tabular-regression |
| tags: |
| - tabular |
| - synthetic |
| - pretraining |
| - in-context-learning |
| size_categories: |
| - 100M<n<1B |
| --- |
| |
| # Tabula Pretraining Corpus v2 |
|
|
| A large-scale synthetic tabular dataset for pretraining transformer-based in-context learning models for tabular data (similar to TabPFN). |
|
|
| ## Overview |
|
|
| | Metric | Value | |
| |--------|-------| |
| | Total rows | 272,271,776 | |
| | Total datasets | 10,867 | |
| | Shards | 135 | |
| | Mean utility AUC | 0.851 | |
| | Format | Parquet (float32) | |
|
|
| ## Schema |
|
|
| Each shard is a Parquet file with a fixed-width schema: |
|
|
| - **feat_0** through **feat_63**: Float32 feature columns. Unused slots are NaN. |
| - **target**: Float32 target variable (classification label or regression target). |
| - **_source_meta**: JSON string with dataset metadata including: |
| - `generator`: Which synthetic generator produced this dataset |
| - `task_type`: "binary", "multiclass", or "regression" |
| - `n_features`: Number of active features (rest are NaN-padded) |
| - `n_classes`: Number of target classes |
| - `n_samples`: Number of rows in the original dataset |
| - `domain`: Semantic domain (finance, health, etc.) |
| - `feature_names`: Original domain-specific column names |
|
|
| ## Generators |
|
|
| | Generator | Datasets | |
| |-----------|----------| |
| | GaussianMixture | 3,029 | |
| | Polynomial | 2,738 | |
| | SCM | 2,674 | |
| | TreePrior | 2,096 | |
| | Regression | 325 | |
| | MixedType_GaussianMixture | 2 | |
| | MixedType_SCM | 2 | |
| | MixedType_TreePrior | 1 | |
| |
| ## Task Types |
| |
| | Type | Datasets | |
| |------|----------| |
| | binary | 8,396 | |
| | multiclass | 2,146 | |
| | regression | 325 | |
| |
| ## Domains |
| |
| | Domain | Datasets | |
| |--------|----------| |
| | hr | 1,033 | |
| | education | 1,031 | |
| | telecom | 1,028 | |
| | science | 1,020 | |
| | iot | 1,005 | |
| | finance | 1,000 | |
| | health | 985 | |
| | ecommerce | 977 | |
| | logistics | 972 | |
| | environment | 935 | |
| | manufacturing | 881 | |
| |
| ## Quality Gates |
| |
| Every generated dataset passes quality gates before inclusion: |
| - **No constant columns** — all features must vary |
| - **No all-null columns** |
| - **Minority class fraction ≥ 5%** for classification |
| - **Duplicate row fraction ≤ 30%** |
| - **RF utility AUC ≥ 0.55** — a Random Forest must achieve above-chance cross-validated AUC |
| |
| Gate failure rate: 22.4% |
| |
| ## Data Augmentation |
| |
| - **Missingness injection**: ~30% of datasets have random missing values injected |
| - **Concept drift**: ~20% of datasets have feature distribution shifts |
| |
| ## Usage |
| |
| ```python |
| from datasets import load_dataset |
|
|
| ds = load_dataset("avewright/tabula-pretraining-corpus-v2", split="train", streaming=True) |
| |
| for batch in ds.iter(batch_size=512): |
| features = batch["feat_0"] # access individual features |
| target = batch["target"] |
| meta = batch["_source_meta"] # JSON metadata string |
| ``` |
| |
| ## License |
|
|
| Apache 2.0 |
|
|
