Update dataset card

README.md

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+---
+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