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@@ -13,6 +13,8 @@ widget:
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  # Arabic GPT2
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  You can find more information in our paper [AraGPT2](https://arxiv.org/abs/2012.15520)
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  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.
@@ -38,7 +40,7 @@ from arabert.aragpt2.grover.modeling_gpt2 import GPT2LMHeadModel
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  from arabert.preprocess import ArabertPreprocessor
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- MODEL_NAME='aragpt2-medium'
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  arabert_prep = ArabertPreprocessor(model_name=MODEL_NAME)
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  text=""
@@ -73,25 +75,7 @@ python create_pretraining_data.py
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  Finetuning:
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  ```bash
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- python3 run_pretraining.py \\
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- --input_file="gs://<GS_BUCKET>/pretraining_data/*" \\
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- --output_dir="gs://<GS_BUCKET>/pretraining_model/" \\
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- --config_file="config/small_hparams.json" \\
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- --batch_size=128 \\
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- --eval_batch_size=8 \\
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- --num_train_steps= \\
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- --num_warmup_steps= \\
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- --learning_rate= \\
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- --save_checkpoints_steps= \\
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- --max_seq_length=1024 \\
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- --max_eval_steps= \\
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- --optimizer="lamb" \\
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- --iterations_per_loop=5000 \\
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- --keep_checkpoint_max=10 \\
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- --use_tpu=True \\
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- --tpu_name=<TPU NAME> \\
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- --do_train=True \\
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- --do_eval=False
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  ```
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  # Model Sizes
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  # Arabic GPT2
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+ <img src="https://raw.githubusercontent.com/aub-mind/arabert/master/AraGPT2.png" width="100" align="left"/>
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+
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  You can find more information in our paper [AraGPT2](https://arxiv.org/abs/2012.15520)
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  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.
 
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  from arabert.preprocess import ArabertPreprocessor
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+ MODEL_NAME='aubmindlab/aragpt2-medium'
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  arabert_prep = ArabertPreprocessor(model_name=MODEL_NAME)
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  text=""
 
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  Finetuning:
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  ```bash
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+ python3 run_pretraining.py \\\n --input_file="gs://<GS_BUCKET>/pretraining_data/*" \\\n --output_dir="gs://<GS_BUCKET>/pretraining_model/" \\\n --config_file="config/small_hparams.json" \\\n --batch_size=128 \\\n --eval_batch_size=8 \\\n --num_train_steps= \\\n --num_warmup_steps= \\\n --learning_rate= \\\n --save_checkpoints_steps= \\\n --max_seq_length=1024 \\\n --max_eval_steps= \\\n --optimizer="lamb" \\\n --iterations_per_loop=5000 \\\n --keep_checkpoint_max=10 \\\n --use_tpu=True \\\n --tpu_name=<TPU NAME> \\\n --do_train=True \\\n --do_eval=False
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
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  # Model Sizes
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