!python /notebooks/seq_2_seq/run_seq2seq_qa.py \
  --model_name_or_path google/mt5-large \
  --dataset_name 9wimu9/SinQuAD \
  --context_column context \
  --question_column question \
  --answer_column answers \
  --do_train \
  --do_eval \
  --per_device_train_batch_size 8 \
  --learning_rate 1e-3 \
  --num_train_epochs 1 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir mt5-large-v1 \
  --logging_steps 1 \
  --bf16 \
  --gradient_accumulation_steps 4 \
  --gradient_checkpointing True \
  --optim adafactor




{
   "eval/loss":0.9061169624328612,
   "_timestamp":1686240530.1377208,
   "_step":370,
   "_runtime":902.276704788208,
   "train/global_step":369,
   "eval/steps_per_second":7.803,
   "train/train_steps_per_second":0.425,
   "_wandb.runtime":902,
   "train/epoch":1,
   "train/total_flos":26479261148774400,
   "train/loss":0.1842,
   "train/train_loss":0.6567919482060565,
   "train/learning_rate":0,
   "train/train_runtime":868.8715,
   "eval/samples_per_second":62.341,
   "train/train_samples_per_second":13.588,
   "eval/runtime":25.12
}
{}
transformers pytorch tensorboard mt5 text2text-generation autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

!python /notebooks/seq_2_seq/run_seq2seq_qa.py \
  --model_name_or_path google/mt5-large \
  --dataset_name 9wimu9/SinQuAD \
  --context_column context \
  --question_column question \
  --answer_column answers \
  --do_train \
  --do_eval \
  --per_device_train_batch_size 8 \
  --learning_rate 1e-3 \
  --num_train_epochs 1 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir mt5-large-v1 \
  --logging_steps 1 \
  --bf16 \
  --gradient_accumulation_steps 4 \
  --gradient_checkpointing True \
  --optim adafactor




{
   "eval/loss":0.9061169624328612,
   "_timestamp":1686240530.1377208,
   "_step":370,
   "_runtime":902.276704788208,
   "train/global_step":369,
   "eval/steps_per_second":7.803,
   "train/train_steps_per_second":0.425,
   "_wandb.runtime":902,
   "train/epoch":1,
   "train/total_flos":26479261148774400,
   "train/loss":0.1842,
   "train/train_loss":0.6567919482060565,
   "train/learning_rate":0,
   "train/train_runtime":868.8715,
   "eval/samples_per_second":62.341,
   "train/train_samples_per_second":13.588,
   "eval/runtime":25.12
}
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
