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--- |
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language: |
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- ar |
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tags: |
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- Arabic T5 |
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- MSA |
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- Twitter |
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- Arabic Dialect |
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- Arabic Machine Translation |
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- Arabic Text Summarization |
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- Arabic News Title and Question Generation |
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- Arabic Paraphrasing and Transliteration |
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- Arabic Code-Switched Translation |
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--- |
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# AraT5-msa-base |
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# AraT5: Text-to-Text Transformers for Arabic Language Generation |
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<img src="https://huggingface.co/UBC-NLP/AraT5-base/resolve/main/AraT5_CR_new.png" alt="AraT5" width="45%" height="35%" align="right"/> |
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This is the repository accompanying our paper [AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation](https://arxiv.org/abs/2109.12068). In this is the repository we Introduce **AraT5<sub>MSA</sub>**, **AraT5<sub>Tweet</sub>**, and **AraT5**: three powerful Arabic-specific text-to-text Transformer based models; |
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--- |
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# How to use AraT5 models |
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Below is an example for fine-tuning **AraT5-base** for News Title Generation on the Aranews dataset |
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``` bash |
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!python run_trainier_seq2seq_huggingface.py \ |
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--learning_rate 5e-5 \ |
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--max_target_length 128 --max_source_length 128 \ |
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--per_device_train_batch_size 8 --per_device_eval_batch_size 8 \ |
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--model_name_or_path "UBC-NLP/AraT5-base" \ |
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--output_dir "/content/AraT5_FT_title_generation" --overwrite_output_dir \ |
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--num_train_epochs 3 \ |
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--train_file "/content/ARGEn_title_genration_sample_train.tsv" \ |
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--validation_file "/content/ARGEn_title_genration_sample_valid.tsv" \ |
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--task "title_generation" --text_column "document" --summary_column "title" \ |
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--load_best_model_at_end --metric_for_best_model "eval_bleu" --greater_is_better True --evaluation_strategy epoch --logging_strategy epoch --predict_with_generate\ |
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--do_train --do_eval |
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``` |
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For more details about the fine-tuning example, please read this notebook [](https://github.com/UBC-NLP/araT5/blob/main/examples/Fine_tuning_AraT5.ipynb) |
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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](https://huggingface.co/UBC-NLP/AraT5-base-title-generation)). |
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For more details, please visit our own [GitHub](https://github.com/UBC-NLP/araT5). |
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# AraT5 Models Checkpoints |
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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).``` |
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| **Model** | **Link** | |
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|---------|:------------------:| |
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| **AraT5-base** | [https://huggingface.co/UBC-NLP/AraT5-base](https://huggingface.co/UBC-NLP/AraT5-base) | |
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| **AraT5-msa-base** | [https://huggingface.co/UBC-NLP/AraT5-msa-base](https://huggingface.co/UBC-NLP/AraT5-msa-base) | |
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| **AraT5-tweet-base** | [https://huggingface.co/UBC-NLP/AraT5-tweet-base](https://huggingface.co/UBC-NLP/AraT5-tweet-base) | |
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| **AraT5-msa-small** | [https://huggingface.co/UBC-NLP/AraT5-msa-small](https://huggingface.co/UBC-NLP/AraT5-msa-small) | |
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| **AraT5-tweet-small**| [https://huggingface.co/UBC-NLP/AraT5-tweet-small](https://huggingface.co/UBC-NLP/AraT5-tweet-small) | |
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# BibTex |
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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): |
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```bibtex |
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@inproceedings{nagoudi-2022-arat5, |
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title = "{AraT5: Text-to-Text Transformers for Arabic Language Generation", |
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author = "Nagoudi, El Moatez Billah and |
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Elmadany, AbdelRahim and |
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Abdul-Mageed, Muhammad", |
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booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics", |
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month = May, |
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year = "2022", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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} |
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``` |
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## Acknowledgments |
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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](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). We also thank the [Google TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) program for providing us with free TPU access. |
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