| --- |
| 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](https://github.com/neospace-ai/NeoLDM) |
| 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 |
|
|
| ```python |
| 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](https://arxiv.org/abs/2011.01843), |
| [github.com/IBM/TabFormer](https://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. |
|
|
