---
title: SALT
canonical_url: "https://www.modelscope.cn/datasets/SAP/SALT"
md_url: "https://www.modelscope.cn/datasets/SAP/SALT.md"
repository: SAP/SALT
last_updated: 2025-11-05
license: cc-by-nc-sa-4.0
storage_size: "115 MB"
downloads: 462
stars: 0
---

# SALT

> SALT - SAP 在 ModelScope 开源的数据集。SALT: Sales Autocompletion Linked Business Tables Dataset

SAP/SALT 是 ModelScope 魔搭社区上的数据集，存储大小 115 MB，采用 cc-by-nc-sa-4.0 许可。

- **Repository**: SAP/SALT
- **License**: cc-by-nc-sa-4.0
- **Storage size**: 115 MB
- **Downloads**: 462
- **Stars**: 0
- **Last updated**: 2025-11-05

Source: https://www.modelscope.cn/datasets/SAP/SALT

---

# SALT: Sales Autocompletion Linked Business Tables Dataset
[![License](https://img.shields.io/badge/license-CC--BY--NC--SA--4.0-blue)]()[![arXiv](https://img.shields.io/badge/arXiv-2501.03413-29d634.svg)](https://arxiv.org/abs/2501.03413)

Dataset for our paper [**SALT: Sales Autocompletion Linked Business Tables Dataset**](https://openreview.net/forum?id=UZbELpkWIr#discussion) presented at [NeurIPS'24 Table Representation Workshop](https://table-representation-learning.github.io/).


#### News
- **07/10/2025: 🎉🎉🎉 Dataset is now integrated into [RelBench](https://github.com/snap-stanford/relbench) 🎉🎉🎉**
- 01/11/2025: Updated paper (some results changed due to minor dataset changes, screenshots added to appendix)
- 12/19/2024: Train/test splits released
- 12/15/2024: Preliminatry dataset now also available on Hugging Face.
- 12/13/2024: Provided data 
- 10/29/2024: Preliminary repository created

### Abstract
Foundation models, particularly those that incorporate Transformer architectures, have demonstrated exceptional performance in domains such as natural language processing and image processing. Adapting these models to structured data, like tables, however, introduces significant challenges. These difficulties are even more pronounced when addressing multi-table data linked via foreign key, which is prevalent in the enterprise realm and crucial for empowering business use cases. Despite its substantial impact, research focusing on such linked business tables within enterprise settings remains a significantly important yet underexplored domain.
To address this, we introduce a curated dataset sourced from an Enterprise Resource Planning (ERP) system, featuring extensive linked tables. This dataset is specifically designed to support research endeavors in table representation learning. By providing access to authentic enterprise data, our goal is to potentially enhance the effectiveness and applicability of models for real-world business contexts.

### Information
![Table Schema of SALT Dataset](https://raw.githubusercontent.com/SAP-samples/salt/master/images/schema.svg "SALT Schema")
*Table Schema of SALT Dataset*

![Screenshot of a Salesorder Input Mask](https://raw.githubusercontent.com/SAP-samples/salt/master/images/SAP_S4HANA_SalesOrder_App.png "Salesorder Input Mask")
*Example Input Mask of a Salesorder App using SAP S/4HANA*

### Usage
Please mind that the following 8 fields are meant to be used as classification labels (all other fields can be used as input features):
- `SALESOFFICE`
- `SALESGROUP`
- `CUSTOMERPAYMENTTERMS`
- `SHIPPINGCONDITION`
- `SHIPPINGPOINT`
- `PLANT`
- `HEADERINCOTERMSCLASSIFICATION`
- `ITEMINCOTERMSCLASSIFICATION`

#### Example of loading the tables with Hugging Face datasets
Unless `datasets` library is already installed, install it with:

```bash
pip install datasets
```

```python
from datasets import load_dataset

dataset_name = "sap-ai-research/SALT"
split = "train"  # use "train" or "test"
salesdocuments = load_dataset(dataset_name, "salesdocuments", split=split)
salesdocument_items = load_dataset(dataset_name, "salesdocument_items", split=split)
customers = load_dataset(dataset_name, "customers", split=split)
addresses = load_dataset(dataset_name, "addresses", split=split)

# you can also load the joined table which combines the four tables in one
joined_table = load_dataset(dataset_name, "joined_table", split=split)

# separate the input features and target fields
target_fields = ["SALESOFFICE", "SALESGROUP", "CUSTOMERPAYMENTTERMS",
                 "SHIPPINGCONDITION", "SHIPPINGPOINT", "PLANT",
                 "HEADERINCOTERMSCLASSIFICATION", "ITEMINCOTERMSCLASSIFICATION"]
dataframe = joined_table.to_pandas()
X = dataframe[[c for c in dataframe.columns if c not in target_fields]]
y = dataframe[target_fields]
```

#### Example of loading the tables with pandas
Unless `pandas` library is already installed, install it with:

```bash
pip install pandas
```

```python
import pandas as pd

# load the table data from the parquet files
salesdocuments = pd.read_parquet("I_SalesDocument_train.parquet")
salesdocument_items = pd.read_parquet("I_SalesDocumentItem_train.parquet")
customers = pd.read_parquet("I_Customer.parquet")
addresses = pd.read_parquet("I_AddrOrgNamePostalAddress.parquet")
joined = pd.read_parquet("JoinedTables_train.parquet")

# show the first elements
salesdocuments.head()
```

### Authors:
 - [Tassilo Klein](https://tjklein.github.io/)
 - [Clemens Biehl](https://www.linkedin.com/in/clemens-biehl-43a39a117/)
 - [Margarida Costa](https://www.linkedin.com/in/mariamargaridacosta/)
 - [André Sreš](https://www.linkedin.com/in/andr%C3%A9-sre%C5%A1-937096160/)
 - [Jonas Kolk](https://www.linkedin.com/in/jonas-kolk-b8a94b123/)
 - [Johannes Hoffart](https://www.linkedin.com/in/johanneshoffart/)

## Citations
If you use this dataset in your research or want to refer to our work, please cite:

```
@inproceedings{
klein2024salt,
title={{SALT}: Sales Autocompletion Linked Business Tables Dataset},
author={Tassilo Klein and Clemens Biehl and Margarida Costa and Andre Sres and Jonas Kolk and Johannes Hoffart},
booktitle={NeurIPS 2024 Third Table Representation Learning Workshop},
year={2024},
url={https://openreview.net/forum?id=UZbELpkWIr}
}
```

## Roadmap
- [x] Integration into [RelBench](https://relbench.stanford.edu/)
- [x] Release dataset