[s2s] distillBART docs for paper replication (#8150)

examples/seq2seq/builtin_trainer/train_distilbart_cnn.sh

@@ -1,19 +1,18 @@
-export WANDB_PROJECT=distilbart-cnn
+export WANDB_PROJECT=distilbart-trainer
 export BS=32
-export GAS=1
 export m=sshleifer/student_cnn_12_6
 export tok=facebook/bart-large
 export MAX_TGT_LEN=142
 
 python finetune_trainer.py \
     --model_name_or_path $m --tokenizer_name $tok \ 
-    --data_dir $CNN_DIR \
+    --data_dir cnn_dm \
     --output_dir distilbart-cnn-12-6 --overwrite_output_dir \
     --learning_rate=3e-5 \
     --warmup_steps 500 --sortish_sampler \
     --fp16 \
     --n_val 500 \
-    --gradient_accumulation_steps=$GAS \
+    --gradient_accumulation_steps=1 \
     --per_device_train_batch_size=$BS --per_device_eval_batch_size=$BS \
     --freeze_encoder --freeze_embeds \
     --num_train_epochs=2 \
@@ -21,5 +20,5 @@ python finetune_trainer.py \
     --logging_first_step \
     --max_target_length 56 --val_max_target_length $MAX_TGT_LEN --test_max_target_length $MAX_TGT_LEN \
     --do_train --do_eval --do_predict --evaluate_during_training \
-    --predict_with_generate \
+    --predict_with_generate --sortish_sampler \
     "$@"