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aubmindlab/aragpt2-base

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Arabic GPT2

You can find more information in our paper AraGPT2

The code in this repository was used to train all GPT2 variants. The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API.

GPT2-base and medium uses the code from the gpt2 folder and can trains models from the minimaxir/gpt-2-simple repository. These models were trained using the lamb optimizer and follow the same architecture as gpt2 and are fully compatible with the transformers library.

GPT2-large and GPT2-mega were trained using the imcaspar/gpt2-ml library, and follow the grover architecture. You can use the pytorch classes found in grover/modeling_gpt2.py as a direct replacement for classes in the transformers library (it should support version v4.x from transformers). Both models are trained using the adafactor optimizer, since the adam and lamb optimizer use too much memory causing the model to not even fit 1 batch on a TPU core.

AraGPT2 is trained on the same large Arabic Dataset as AraBERTv2.

Usage

Testing the model using `transformers`:

python

from transformers import GPT2TokenizerFast, pipeline
#for base and medium
from transformers import GPT2LMHeadModel
#for large and mega
# pip install arabert
from arabert.aragpt2.grover.modeling_gpt2 import GPT2LMHeadModel

from arabert.preprocess import ArabertPreprocessor

MODEL_NAME='aubmindlab/aragpt2-base'
arabert_prep = ArabertPreprocessor(model_name=MODEL_NAME)

text=""
text_clean = arabert_prep.preprocess(text)

model = GPT2LMHeadModel.from_pretrained(MODEL_NAME)
tokenizer = GPT2TokenizerFast.from_pretrained(MODEL_NAME)
generation_pipeline = pipeline("text-generation",model=model,tokenizer=tokenizer)

#feel free to try different decoding settings
generation_pipeline(text,
    pad_token_id=tokenizer.eos_token_id,
    num_beams=10,
    max_length=200,
    top_p=0.9,
    repetition_penalty = 3.0,
    no_repeat_ngram_size = 3)[0]['generated_text']

Finetunning using `transformers`:

Follow the guide linked here

Finetuning using our code with TF 1.15.4:

Create the Training TFRecords:

bash

python create_pretraining_data.py
 --input_file=<RAW TEXT FILE with documents/article separated by an empty line>
 --output_file=<OUTPUT TFRecord>
 --tokenizer_dir=<Directory with the GPT2 Tokenizer files>

Finetuning:

bash

 python3 run_pretraining.py \\r\n --input_file="gs://<GS_BUCKET>/pretraining_data/*" \\r\n --output_dir="gs://<GS_BUCKET>/pretraining_model/" \\r\n --config_file="config/small_hparams.json" \\r\n --batch_size=128 \\r\n --eval_batch_size=8 \\r\n --num_train_steps= \\r\n --num_warmup_steps= \\r\n --learning_rate= \\r\n --save_checkpoints_steps= \\r\n --max_seq_length=1024 \\r\n --max_eval_steps= \\r\n --optimizer="lamb" \\r\n --iterations_per_loop=5000 \\r\n --keep_checkpoint_max=10 \\r\n --use_tpu=True \\r\n --tpu_name=<TPU NAME> \\r\n --do_train=True \\r\n --do_eval=False

Model Sizes

Model	Optimizer	Context size	Embedding Size	Num of heads	Num of layers	Model Size / Num of Params
AraGPT2-base	`lamb`	1024	768	12	12	527MB / 135M
AraGPT2-medium	`lamb`	1024	1024	16	24	1.38G/370M
AraGPT2-large	`adafactor`	1024	1280	20	36	2.98GB/792M
AraGPT2-mega	`adafactor`	1024	1536	25	48	5.5GB/1.46B

All models are available in the HuggingFace model page under the aubmindlab name. Checkpoints are available in PyTorch, TF2 and TF1 formats.

Compute

Model	Hardware	num of examples (seq len = 1024)	Batch Size	Num of Steps	Time (in days)
AraGPT2-base	TPUv3-128	9.7M	1792	125K	1.5
AraGPT2-medium	TPUv3-8	9.7M	1152	85K	1.5
AraGPT2-large	TPUv3-128	9.7M	256	220k	3
AraGPT2-mega	TPUv3-128	9.7M	256	780K	9

Dataset

The pretraining data used for the new AraGPT2 model is also used for AraBERTv2 and AraELECTRA.

The dataset consists of 77GB or 200,095,961 lines or 8,655,948,860 words or 82,232,988,358 chars (before applying Farasa Segmentation)

For the new dataset we added the unshuffled OSCAR corpus after we thoroughly filter it, to the dataset used in AraBERTv1 but without the websites that we previously crawled:

—OSCAR unshuffled and filtered.
—Arabic Wikipedia dump from 2020/09/01
—The 1.5B words Arabic Corpus
—The OSIAN Corpus
—Assafir news articles. Huge thank you for Assafir for giving us the data

Disclaimer

The text generated by AraGPT2 is automatically generated by a neural network model trained on a large amount of texts, which does not represent the authors' or their institutes' official attitudes and preferences. The text generated by AraGPT2 should only be used for research and scientific purposes. If it infringes on your rights and interests or violates social morality, please do not propagate it.

If you used this model please cite us as :

@inproceedings{antoun-etal-2021-aragpt2,
    title = "{A}ra{GPT}2: Pre-Trained Transformer for {A}rabic Language Generation",
    author = "Antoun, Wissam  and
      Baly, Fady  and
      Hajj, Hazem",
    booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
    month = apr,
    year = "2021",
    address = "Kyiv, Ukraine (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.wanlp-1.21",
    pages = "196--207",
}

Acknowledgments

Thanks to TensorFlow Research Cloud (TFRC) for the free access to Cloud TPUs, couldn't have done it without this program, and to the AUB MIND Lab Members for the continuous support. Also thanks to Yakshof and Assafir for data and storage access. Another thanks for Habib Rahal (https://www.behance.net/rahalhabib), for putting a face to AraBERT.

Contacts

Wissam Antoun: Linkedin | Twitter | Github | <wfa07@mail.aub.edu> | <wissam.antoun@gmail.com>

Fady Baly: Linkedin | Twitter | Github | <fgb06@mail.aub.edu> | <baly.fady@gmail.com>