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---
license: apache-2.0
task_categories:
- text-generation
language:
- zh
pretty_name: MD2T
size_categories:
- 100K<n<1M
---
MD2T is a new setting for multimodal E-commerce Description generation based on structured keywords and images.
Our paper (LREC-COLING 2024): [A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation](https://arxiv.org/abs/2402.13587).
# MD2T Dataset Statistics
| MD2T | Cases&Bags | Clothing | Home Appliances |
|-----------|------------|----------|-----------------|
| #Train | 18,711 | 200,000 | 86,858 |
| #Dev | 983 | 6,120 | 1,794 |
| #Test | 1,000 | 8,700 | 2,200 |
| Avg_N #MP | 5.41 | 6.57 | 5.48 |
| Avg_L #MP | 13.50 | 20.34 | 18.30 |
| Avg_L #Desp | 80.05 | 79.03 | 80.13 |
**Table 1:** The detailed statistics of MD2T. Avg_N and Avg_L represent the average number and length respectively. MP and Desp indicate the marketing keywords and description.
# Cite our Work
```
@article{li2024multimodal,
title={A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation},
author={Li, Yunxin and Hu, Baotian and Luo, Wenhan and Ma, Lin and Ding, Yuxin and Zhang, Min},
journal={arXiv preprint arXiv:2402.13587},
year={2024}
}
``` |