GAVI: A Category-Aware Generative Approach for Brand Value Identification

This repository contains the source code used in our paper: GAVI: A Category-Aware Generative Approach for Brand Value Identification.

Installation

1. Clone the repository
2. Download the openbrand-dataset and place the az_base_dataset.jsonl file in the datasets folder of this repo.
3. Install the required dependencies in the requirements.txt file:

   $ pip install -r requirements.txt
   

Model Training

To train the model for the brand value identification task using the ./datasets/az_base_dataset.jsonl, run the following shell script:

$ bash ./train.sh

The model uses the pre-trained t5-base model from the in the 🤗 Transformers by default to train the model. The trained model will be stored in ./saved_models/.

Download Pre-trained Model

We provide a pre-trained model on the az_base_dataset.jsonl dataset here. Download the folder and place it in the saved_models folder of the repo.

After running all scripts, you should obtain the following directory tree:

├── README.md
├── config.py
├── datasets
│   └── az_base_dataset.jsonl
├── saved_models
│   └── gavi
│       └── config.json
│       └── generation_config.json
│       └── pytorch_model.bin
├── data.py
├── train.py
├── requirements.txt
├── train.sh
├── test.py

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If you found this work useful, please cite it as follows:

@inproceedings{sabeh-etal-2023-gavi,
    title = "{GAVI}: A Category-Aware Generative Approach for Brand Value Identification",
    author = "Sabeh, Kassem  and
      Kacimi, Mouna  and
      Gamper, Johann",
    editor = "Abbas, Mourad  and
      Freihat, Abed Alhakim",
    booktitle = "Proceedings of the 6th International Conference on Natural Language and Speech Processing (ICNLSP 2023)",
    month = dec,
    year = "2023",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.icnlsp-1.11",
    pages = "110--119",
}