ArBanking77: Intent Detection Neural Model and a New Dataset in Modern and Dialectical Arabic

ArBanking77 is an MSA and Dialectal Arabic Corpus for Arabic Intent Detection in Banking Domain. It consists of 31,404 samples (MSA and Palestinian dialects). This repo contains the source-code and sample dataset to train and evaluate Arabic Intent Detection model.

ArBanking77 Corpus

ArBanking77 consists of 31,404 (MSA and Palestinian dialects) that are manually Arabized and localized from the original English Banking77 dataset; which consists of 13,083 queries. Each query is classified into one of the 77 classes ( intents) including card arrival, card linking, exchange rate, and automatic top-up. You can find the list of these 77 intents in the ./data/Banking77_intents.csv file. A neural model based on AraBERT was fine-tuned on the ArBanking77 dataset (F1-score 92% for MSA, 90% for PAL)

Full Corpus Download

A sample data is available in the data directory. However, the entire ArBanking77 corpus is available to download upon request for academic and commercial use. However, we cannot provide the augmented data.

Request to download ArBanking77 (corpus and the model)

Model Download

SinaLab HuggingFace

Online Demo

You can try our model using this demo link.

Requirements

At this point, the code is compatible with Python 3.11

Clone this repo

git clone https://github.com/SinaLab/ArabicNER.git

This package has dependencies on multiple Python packages. It is recommended to Conda to create a new environment that mimics the same environment the model was trained in. Provided in this repo requirements.txt from which you can create a new conda environment using the command below.

conda create -n env_name python=3.11

Install requirements using pip command:

pip install -r requirements.txt

Project Structure

.
├── data                            <- data dir
│   ├── Banking77_Arabized_MSA_PAL_train_sample.csv
│   ├── Banking77_Arabized_MSA_PAL_val_sample.csv
│   ├── Banking77_Arabized_MSA_test_sample.csv
│   ├── Banking77_Arabized_PAL_test_sample.csv
│   ├── Banking77_intents.csv
├── outputs
│   ├── models                      <- trained models
│   ├── results                     <- evaluation results and reports
├── src                             <- training and evaluation scripts
│   ├── run_glue_no_trainer.py
│   ├── run_glue_no_trainer_eval.py
│   └── utils.py
├── .gitignore
├── LICENSE
├── README.md
└── requirements.txt

Model Training

You can start model training by running the following command. It's recommended to pass the arguments demonstrated below to get results similar to the ones reported in the paper.

python ./src/run_glue_no_trainer.py
    --model_name_or_path aubmindlab/bert-base-arabertv02 
    --train_file ./data/Banking77_Arabized_MSA_PAL_train_sample.csv
    --validation_file ./data/Banking77_Arabized_MSA_PAL_val_sample.csv 
    --seed 42 
    --max_length 128 
    --learning_rate 4e-5 
    --num_train_epochs 20 
    --per_device_train_batch_size 64 
    --output_dir ./outputs/models

Evaluation

Additionally, you can evaluate the trained model on Banking77_Arabized_MSA_test_sample.csv and Banking77_Arabized_PAL_test_sample.csv test sets as follows:

python ./src/run_glue_no_trainer_eval.py 
    --model_name_or_path ./outputs/models 
    --validation_file ./data/Banking77_Arabized_MSA_test_sample.csv 
    --seed 42 
    --per_device_eval_batch_size 64 
    --results_dir ./outputs/results 
    --log_path ./outputs/logs/log.txt

Credits

This research was funded by the Palestinian Higher Council for Innovation and Excellence and the Scientific and Technological Research Council of Türkiye (TÜBİTAK) under project No. 120N761 - CONVERSER: Conversational AI System for Arabic.

Citation

Mustafa Jarrar, Ahmet Birim, Mohammed Khalilia, Mustafa Erden, and Sana Ghanem: ArBanking77: Intent Detection Neural Model and a New Dataset in Modern and Dialectical Arabic. In Proceedings of the 1st Arabic Natural Language Processing Conference (ArabicNLP), Part of the EMNLP 2023. ACL.

https://arxiv.org/abs/2310.19034