https://github.com/Alibaba-NLP/EcomGPT

An Instruction-tuned Large Language Model for E-commerce
https://github.com/Alibaba-NLP/EcomGPT

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An Instruction-tuned Large Language Model for E-commerce

• Host: GitHub
• URL: https://github.com/Alibaba-NLP/EcomGPT
• Owner: Alibaba-NLP
• Created: 2023-08-21T03:49:35.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-26T23:04:03.000Z (about 1 year ago)
• Last Synced: 2024-08-02T06:19:20.255Z (4 months ago)
• Language: Python
• Size: 4.89 MB
• Stars: 209
• Watchers: 5
• Forks: 13
• Open Issues: 10
• Metadata Files:
  • Readme: README.md

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README

        






# An Instruction-Following Large Language Model For E-commerce

![](https://img.shields.io/badge/version-1.0.0-blue)[![Pytorch](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?e&logo=PyTorch&logoColor=white)](https://pytorch.org/)[![arxiv badge](https://img.shields.io/badge/arxiv-2308.06966-red)](https://arxiv.org/pdf/2308.06966.pdf)

Repo for [*EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce*](https://arxiv.org/pdf/2308.06966)

- **we proposed the first E-commerce instruction dataset EcomInstruct, with a total of 2.5 million instruction data**.

- EcomInstruct scales up the data size and task diversity by constructing **atomic tasks with E-commerce basic data types**, such as product information, user reviews. Atomic tasks are defined as intermediate tasks implicitly involved in solving a final task, which we also call Chain-of-Task tasks. 

- We developed EcomGPT by training the backbone model BLOOMZ with the EcomInstruct. **Benefiting from the fundamental semantic understanding capabilities acquired from the Chain-of-Task tasks, EcomGPT exhibits excellent zero-shot generalization capabilities.**



    



## 💡 Perfomance

We perform a human evaluation on EcomGPT and ChatGPT using 12 E-commerce held-out datasets. EcomGPT outperforms or tied ChatGPT on 12 datasets.







## 🛠 Dependencies

```bash

pip install -r requirement.txt

```

#### Details

- Python (>= 3.7)

- [PyTorch](http://pytorch.org/) (>= 2.0.0)

- numpy

- [Transformers](http://huggingface.co/transformers/) (>= 4.27.4)

- seqeval

- rouge

## 💻 Model

The EcomGPT (7b1) is available at [*ModelScope*](https://www.modelscope.cn/models/damo/nlp_ecomgpt_multilingual-7B-ecom/summary). 

## 📚 Dataset (EcomInstruct)

We first open source 12 evaluation datasets. To ensure evaluation efficiency, each evaluation dataset is sampled with only 500 instances.

| Dataset   | Lang. | Task                          | Metric    |

| :-------- | :---- | :---------------------------- | :-------- |

| Lenove    | EN    | Named Entity Recognization    | F1, Rouge |

| Lenove    | EN    | Entity Span Detection         | Rouge     |

| Reddit    | EN    | Extractive QA                 | Rouge     |

| ABSA      | EN    | Review Topic Classification   | F1, Rouge |

| MEPAVE    | ZH    | Attribute Value Recognization | F1, Rouge |

| MEPAVE    | ZH    | Attribute Value Detection     | Rouge     |

| Multi-CPR | ZH    | Product Select                | Rouge     |

| Multi-CPR | ZH    | Product Align                 | F1, Rouge |

| OpenBG    | ZH    | Title Attritube Matching      | F1, Rouge |

| OpenBG    | ZH    | Fine-grain Product Classify   | F1, Rouge |

| OpenBG    | ZH    | Coarse-grain Product Classify | F1, Rouge |

| OpenBG    | ZH    | Title Generate                | Rouge     |

The dataset files **satisfy the following file hierarchy**:

```

.

├── [Dataset Name]

│   └── tasks

│       └── [task name]

│           ├── meta-info.json

│           └── test.json

...

└── Reddit_QA

    └── tasks

        └── EN-Reddit_QA-Extract-Extract_QA

            ├── meta-info.json

            └── test.json

```

## 🔍 Evaluation

One can evaluate the performance of EcomGPT with the following command：

```bash

python eval.py -tf ./test_tasks.txt -m [model name or path] -sn [result file name] -bdd [base dataset dir]

```

## 🔥 TODO

- Open Source Weight of EcomGPT ✅

## 📄 Citation

If you found this work useful, consider giving this repository a star and citing our paper as followed:

```bigquery

@article{li2023ecomgpt,

  title={EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce},

  author={Li, Yangning and Ma, Shirong and Wang, Xiaobin and Huang, Shen and Jiang, Chengyue and Zheng, Hai-Tao and Xie, Pengjun and Huang, Fei and Jiang, Yong},

  journal={arXiv preprint arXiv:2308.06966},

  year={2023}

}

```