luizcoroo/cortex-ibm-tabformer-embeddings

cortex embeddings — IBM TabFormer

Per-transaction hidden states (128-d, float32) produced by Neospace's cortex transaction foundation model over every transaction in the public IBM TabFormer credit-card dataset (card_transaction.v1.csv, 24,386,900 transactions, 2,000 cardholders, 1991–2020).

These embeddings let you reproduce the NeoLDM benchmark without running cortex: feed them to the gradient-boosted-tree fraud classifier in that repo.

Contents

dir	checkpoint	rows
pretrain/	self-supervised (full-MLM; never saw is_fraud)	24,386,900
finetune/	supervised (finetuned on is_fraud)	24,386,900

Each parquet has three columns:

• user — cardholder id (matches the IBM TabFormer User field).
• timestamp__orig — transaction timestamp (UTC, minute resolution).
• hidden — the 128-d cortex hidden state (list<float>[128], float32).

Join key to the raw transactions: (user, timestamp__orig). The encoding is byte_stream_split

• zstd; precision is unchanged (float32), so results reproduce bit-for-bit.

Usage

from huggingface_hub import snapshot_download
snapshot_download(repo_id="<your-hf-org>/cortex-ibm-tabformer-embeddings", repo_type="dataset",
                  local_dir="artifacts/embeddings", allow_patterns=["pretrain/*", "finetune/*"])

Then point the NeoLDM scripts at artifacts/embeddings/pretrain (nb02) and artifacts/embeddings/finetune (nb03). See the repo's README for the full pipeline.

License & attribution

The embeddings are derived from the IBM TabFormer dataset (Padhi et al., Tabular Transformers for Modeling Multivariate Time Series, arXiv:2011.01843, github.com/IBM/TabFormer) and are subject to its terms. They are learned representations, not the raw transaction fields. The NeoLDM code is Apache-2.0.