---
license: apache-2.0
---
# TabM Model Instructions

## Model Overview

### 1.1 Method Description
This implementation utilizes the approach from the research paper:  
**"TabM: Advancing Tabular Deep Learning With Parameter-Efficient Ensembling" (ICLR 2025)**

- **Paper Link**: https://arxiv.org/abs/2410.24210
- **Original Repository**: https://github.com/yandex-research/tabm

### 1.2 Motivation
Compared to traditional machine learning methods used in NeoRanking, we aim to explore the performance of Tabular Deep Learning approaches on this type of data for classification tasks.

## Training Details

### 2.1 Training Data
The detailed training dataset is located at: `data/tabm_train.tsv`

### 2.2 Training Parameters
- Model parameters were optimized using the `hyper` framework for parameter tuning
- Training script: `src/tabm_train.py`
- Testing script: `src/tabm_test.py`

### 2.3 Testing Data
- Test dataset: `data/tabm_test.tsv`
- Evaluation metrics have been updated in `spaces/leaderboard`

## Model Usage

### 3.1 Installation
```bash
pip3 install tabm
```

### 3.2 Training
```bash
bash scripts/tabm_train.sh
```

### 3.3 Testing
```bash
bash scripts/tabm_test.sh
```

## Special Notes

Our use of TabM fully complies with the **Apache-2.0 license**. If you need to reference or reuse this model, please adhere to the original author's citation requirements and properly attribute the source.