Datasets:
license: other
license_name: ibm-tabformer-derived
pretty_name: cortex embeddings for IBM TabFormer fraud benchmark
tags:
- tabular
- fraud-detection
- embeddings
- transaction-data
size_categories:
- 10M<n<100M
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 TabFormerUserfield).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.