run_summarization.py t5 model output inconsistent results
Author: menghongtao
Pull request: no

I am working on T5 finetune model , and I found the [t5-small-finetuned-samsum-en](https://huggingface.co/santiviquez/t5-small-finetuned-samsum-en)  model on huggingface and the rouge metric on https://paperswithcode.com/sota/summarization-on-samsum
It shows: (I have delete other models except t5-small-finetuned-samsum-en)

Rank | Model | ROUGE-1 | ROUGE-2 | ROUGE-L | ROUGE-LSUM | gen_len | loss | Details | Year | Tags
-- | -- | -- | -- | -- | -- | -- | -- | -- | -- | --
12 | t5-small-finetuned-samsum-en | 40.039 | 15.85 | 31.808 | 36.089 | 18.107 | 2.192 |   | 2022

But when I use transformers examples code run_summarization.py run the model I have download from huggingface [t5-small-finetuned-samsum-en](https://huggingface.co/santiviquez/t5-small-finetuned-samsum-en) , The result is not consistent with above.
My result is: my ROUGE-1 is 3.34 but the result above is 40.039, It is very different. Here is my result: (The value should *100)
***** eval metrics *****
  eval_gen_len                  =    12.2066
  eval_loss                     =     7.7219
  eval_rouge1_high_fmeasure     =     0.0383
  eval_rouge1_high_precision    =     0.2029
  eval_rouge1_high_recall       =     0.0225
  eval_rouge1_low_fmeasure      =     0.0334
  eval_rouge1_low_precision     =     0.1792
  eval_rouge1_low_recall        =     0.0194
  eval_rouge1_mid_fmeasure      =     0.0358
  eval_rouge1_mid_precision     =     0.1909
  eval_rouge1_mid_recall        =     0.0209
  eval_rouge2_high_fmeasure     =     0.0021
  eval_rouge2_high_precision    =     0.0114
  eval_rouge2_high_recall       =     0.0012
  eval_rouge2_low_fmeasure      =     0.0011
  eval_rouge2_low_precision     =      0.006
  eval_rouge2_low_recall        =     0.0006
  eval_rouge2_mid_fmeasure      =     0.0016
  eval_rouge2_mid_precision     =     0.0086
  eval_rouge2_mid_recall        =     0.0009
  eval_rougeL_high_fmeasure     =     0.0334
  eval_rougeL_high_precision    =     0.1803
  eval_rougeL_high_recall       =     0.0197
  eval_rougeL_low_fmeasure      =     0.0293
  eval_rougeL_low_precision     =     0.1587
  eval_rougeL_low_recall        =     0.0171
  eval_rougeL_mid_fmeasure      =     0.0314
  eval_rougeL_mid_precision     =     0.1688
  eval_rougeL_mid_recall        =     0.0184
  eval_rougeLsum_high_fmeasure  =     0.0362
  eval_rougeLsum_high_precision =     0.1951
  eval_rougeLsum_high_recall    =     0.0213
  eval_rougeLsum_low_fmeasure   =     0.0317
  eval_rougeLsum_low_precision  =     0.1708
  eval_rougeLsum_low_recall     =     0.0184
  eval_rougeLsum_mid_fmeasure   =      0.034
  eval_rougeLsum_mid_precision  =     0.1832
  eval_rougeLsum_mid_recall     =     0.0199
  eval_runtime                  = 0:00:54.57
  eval_samples                  =        818
  eval_samples_per_second       =     14.988
  eval_steps_per_second         =     14.988
And I have not change the run_summarization.py script ,
 My shell script is:
 python ./run_summarization_.py \
    --model_name_or_path $T5_DIR \
    --do_eval \
    --source_prefix "summarize: " \
    --output_dir ./output/huggingface-summarization \
    --dataset_name $samsum_path \
    --dataset_config "3.0.0" \
    --per_device_train_batch_size=1 \
    --per_device_eval_batch_size=1 \
    --overwrite_output_dir \
    --predict_with_generate \
  
 Can anyone help me on this issue? Thanks a lot.

Comments:
Hey 🤗  thanks for opening an issue! We try to keep the github issues for bugs/feature requests. 
Could you ask your question on the [forum](https://discuss.huggingface.co/) instead? I'm sure the community will be of help!
Even better, ask the author of this model in the community tab [here](https://huggingface.co/santiviquez/t5-small-finetuned-samsum-en/discussions)

Hi Arthur, thanks for your reply, I have ask this quention on huggingface discussion.

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/main/CONTRIBUTING.md) are likely to be ignored.