Error using t5-base-cnn
I'm trying to fine-tune all the t5 models over CNN/DailyMail to see how they perform compared to the BART ones. I came across your t5-base-cnn model day. I tried using it in the way mentioned but got interrupted by an error that says : 

OSError: Model name 'sshleifer/t5-base-cnn' was not found in tokenizers model name list (t5-small, t5-base, t5-large, t5-3b, t5-11b). We assumed 'sshleifer/t5-base-cnn' was a path, a model identifier, or url to a directory containing vocabulary files named ['spiece.model'] but couldn't find such vocabulary files at this path or url.

Earlier when I fine-tuned t5-small over CNN, in the output file there was this spiece.model but it's not present in your listed files.

Any suggestions how to get over this so that I can use your t5-base-cnn instead of fine-tuning all-over myself?

Thanks.

@sshleifer 
Use the standard `T5Tokenizer.from_pretrained('t5-base')`<|||||>and I would love to hear your results!<|||||>The outputs of t5-base-cnn are good! Will let you know when I run over a bigger dataset.
My doubt is how many epochs is ideal for fine-tuning t5 models over cnn/dm?
My t5-small fine-tuned over cnn/dm code(number of epochs ran : 1) produces okayish results but not that great. Even some sentences in the output didn't get completed at the end and looked as if it's cut.<|||||>I don't know how many epochs to train t5-small for, but our new `finetune.py` tracks the validation rouge 2 score. Usually when that stops increasing, the model will not further improve.
Also, since epochs are so long, there is a `--val_check_interval` argument that you can use to check this statistic more frequently than the default, every epoch.<|||||>Thank you, Sam. You solve my problem too. Benefit a lot from your work.<|||||>Happy to help. Closing this for now, feel free to open a new issue if you run into more problems!<|||||>Hello everyone!

I used direct T5Tokenizer.from_pretrained('t5-base')... however, I got the following error:

OSError: Model name 't5-base' was not found in tokenizers model name list (t5-small, t5-base, t5-large, t5-3b, t5-11b). We assumed 't5-base' was a path, a model identifier, or url to a  directory containing vocabulary files named ['spiece.model'] but couldn't find such vocabulary files at this path or url.

How can I resolve this issue? or from where can I download the model manually?

Thank you in advance
<|||||>I tried to generate results using ```sshleifer/t5-base-cnn``` and changed the tokenizer to ```tokenizer = T5Tokenizer.from_pretrained('t5-base')``` and failed. 

My code:
```
python run_eval.py sshleifer/t5-base-cnn $DATA_DIR/test.source $OUTPUT_FILE \
    --reference_path $DATA_DIR/test.target \
    --task summarization \
    --device cuda \
    --fp16 \
    --bs 32 \
```

Following is the error message:

```
Traceback (most recent call last):
  File "run_eval.py", line 127, in <module>
    run_generate()
  File "run_eval.py", line 112, in run_generate
    checkpoint_path=args.checkpoint_path,
  File "run_eval.py", line 63, in generate_summaries_or_translations
    **gen_kwargs,
  File "/home/rachelzheng/www-joy/venv/lib/python3.6/site-packages/torch/autograd/grad_mode.py", line 49, in decorate_no_grad
    return func(*args, **kwargs)
  File "/home/rachelzheng/www-joy/venv/lib/python3.6/site-packages/transformers/generation_utils.py", line 480, in generate
    model_kwargs=model_kwargs,
  File "/home/rachelzheng/www-joy/venv/lib/python3.6/site-packages/transformers/generation_utils.py", line 795, in _generate_beam_search
    input_ids = torch.cat([input_ids, beam_tokens.unsqueeze(1)], dim=-1)
RuntimeError: cuda runtime error (710) : device-side assert triggered at /pytorch/aten/src/THC/generic/THCTensorMath.cu:196
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:60: lambda [](int)->auto::operator()(int)->auto: block: [0,0,0], thread:[32,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
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```<|||||>Okay it looks like ```fp16``` leads to the problem. Remove ```--fp16``` solves my problem.