metadata

language:
  - ar
tags:
  - Arabic T5
  - MSA
  - Twitter
  - Arabic Dialect
  - Arabic Machine Translation
  - Arabic Text Summarization
  - Arabic News Title and Question Generation
  - Arabic Paraphrasing and Transliteration
  - Arabic Code-Switched Translation

AraT5-msa-small

AraT5: Text-to-Text Transformers for Arabic Language Generation

This is the repository accompanying our paper AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation. In this is the repository we Introduce AraT5_MSA, AraT5_Tweet, and AraT5: three powerful Arabic-specific text-to-text Transformer based models;

How to use AraT5 models

Below is an example for fine-tuning AraT5-base for News Title Generation on the Aranews dataset

!python run_trainier_seq2seq_huggingface.py \
        --learning_rate 5e-5 \
        --max_target_length 128 --max_source_length 128 \
        --per_device_train_batch_size 8 --per_device_eval_batch_size 8 \
        --model_name_or_path "UBC-NLP/AraT5-base" \
        --output_dir "/content/AraT5_FT_title_generation" --overwrite_output_dir \
        --num_train_epochs 3 \
        --train_file "/content/ARGEn_title_genration_sample_train.tsv" \
        --validation_file "/content/ARGEn_title_genration_sample_valid.tsv" \
        --task "title_generation" --text_column "document" --summary_column "title" \
        --load_best_model_at_end --metric_for_best_model "eval_bleu" --greater_is_better True --evaluation_strategy epoch --logging_strategy epoch --predict_with_generate\
        --do_train --do_eval

For more details about the fine-tuning example, please read this notebook

In addition, we release the fine-tuned checkpoint of the News Title Generation (NGT) which is described in the paper. The model available at Huggingface (UBC-NLP/AraT5-base-title-generation).

For more details, please visit our own GitHub.

AraT5 Models Checkpoints

AraT5 Pytorch and TensorFlow checkpoints are available on the Huggingface website for direct download and use exclusively for research. For commercial use, please contact the authors via email @ (muhammad.mageed[at]ubc[dot]ca).

Model	Link
AraT5-base	https://huggingface.co./UBC-NLP/AraT5-base
AraT5-msa-base	https://huggingface.co./UBC-NLP/AraT5-msa-base
AraT5-tweet-base	https://huggingface.co./UBC-NLP/AraT5-tweet-base
AraT5-msa-small	https://huggingface.co./UBC-NLP/AraT5-msa-small
AraT5-tweet-small	https://huggingface.co./UBC-NLP/AraT5-tweet-small

BibTex

If you use our models (Arat5-base, Arat5-msa-base, Arat5-tweet-base, Arat5-msa-small, or Arat5-tweet-small ) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):

@inproceedings{nagoudi2022_arat5,
  title={AraT5: Text-to-Text Transformers for Arabic Language Generation},
  author={Nagoudi, El Moatez Billah and Elmadany, AbdelRahim and Abdul-Mageed, Muhammad},
  journal={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistic},
  month = {May},
  address = {Online},
  year={2022},
  publisher = {Association for Computational Linguistics}
}

Acknowledgments

We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access.