Finetune M2M on multiple language pairs
Hi @all, I am able to finetune the M2M model for one lang pair using the below script.

CUDA_VISIBLE_DEVICES=0,1,2,3,6 python -m torch.distributed.run --nproc_per_node=5 run_translation.py --model_name_or_path=m2m100_418M_new_token --do_train --do_eval --source_lang ja --target_lang en --fp16=True --evaluation_strategy epoch --output_dir bigfrall --per_device_train_batch_size=48 --per_device_eval_batch_size=48 --overwrite_output_dir --forced_bos_token “en” --train_file orig_manga/orig/train_exp_frame_50k.json --validation_file orig_manga/orig/valid_exp_frame_50k.json --tokenizer_name tokenizer_new_token --num_train_epochs 50 --save_total_limit=5 --save_strategy=epoch --load_best_model_at_end=True --predict_with_generate

But I do not find a solution to finetune the model on more than one language pair simultaneously which I could easily do using FairSeq.
E.g. now I want to finetune it on ja-en and ja-zh pairs. How to pass these both languages? I could not find an option in the run_translation.py script. Please help.
hi, I don't think it's possible :( But you plan to make your model public?<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.<|||||>I also want to do this, do you solve it? @Jourdelune <|||||>No :(<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.