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:

from transformers import GPT2TokenizerFast, pipeline
#for base and medium
from transformers import GPT2LMHeadModel
#for large and mega
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 decodinn 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:

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

Finetuning:

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:

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 continous 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

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