An Instruction-Following Large Language Model For E-commerce

Repo for EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce

• 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

pip install -r requirement.txt

Details

• Python (>= 3.7)
• PyTorch (>= 2.0.0)
• numpy
• Transformers (>= 4.27.4)
• seqeval
• rouge

💻 Model

The EcomGPT (7b1) is available at ModelScope.

📚 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：

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:

@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}
}