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train_summarize.sh
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python -m torch.distributed.launch --nnodes=1 --nproc_per_node=8 --node_rank=0 transformers/examples/pytorch/summarization/run_summarization.py \
--model_name_or_path "facebook/mbart-large-50" \
--do_eval \
--do_train \
--do_predict \
--dataset_name mlsum \
--dataset_config "tu" \
--output_dir ./eval-tr-bart-large/ \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 4 \
--predict_with_generate \
--evaluation_strategy epoch \
--save_strategy epoch \
--num_beams 4 \
--source_lang tr_TR \
--target_lang tr_TR \
--forced_bos_token tr_TR \
--max_target_length 64 \
--max_source_length 496 \
--num_train_epochs 10 \
--load_best_model_at_end \
--metric_for_best_model eval_rougeL \
--learning_rate 5e-05 \
--group_by_length \
--report_to tensorboard \
--label_smoothing_factor 0.1 \
--fp16
python -m torch.distributed.launch --nnodes=1 --nproc_per_node=8 --node_rank=0 transformers/examples/pytorch/summarization/run_summarization.py \
--model_name_or_path "google/mt5-base" \
--do_eval \
--do_train \
--do_predict \
--dataset_name mlsum \
--dataset_config "tu" \
--output_dir ./eval-mt5-base-aggressive/ \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 4 \
--predict_with_generate \
--evaluation_strategy epoch \
--save_strategy epoch \
--num_beams 4 \
--max_target_length 64 \
--max_source_length 496 \
--num_train_epochs 10 \
--learning_rate 5e-04 \
--load_best_model_at_end \
--metric_for_best_model eval_rougeL \
--group_by_length \
--report_to tensorboard \
--source_prefix "summarize: " \
--label_smoothing_factor 0.1
python -m torch.distributed.launch --nnodes=1 --nproc_per_node=8 --node_rank=0 transformers/examples/pytorch/summarization/run_summarization.py \
--model_name_or_path "./turkish-bart-uncased" \
--do_eval \
--do_train \
--do_predict \
--dataset_name mlsum \
--dataset_config "tu" \
--output_dir ./eval-turkish-bart-uncased/ \
--per_device_train_batch_size 4 \
--per_device_eval_batch_size 8 \
--gradient_accumulation_steps 2 \
--predict_with_generate \
--evaluation_strategy epoch \
--save_strategy epoch \
--num_beams 4 \
--max_target_length 64 \
--max_source_length 768 \
--num_train_epochs 15 \
--learning_rate 1e-4 \
--load_best_model_at_end \
--metric_for_best_model eval_rougeL \
--group_by_length \
--report_to tensorboard \
--label_smoothing_factor 0.1 \
--fp16