Unable to evaluate on fine-tuned bart for summarization
Hello,

I used the [finetune_bart.sh ](https://github.com/huggingface/transformers/blob/master/examples/summarization/finetune_bart.sh) and was able to finetune the model on my task. I checked the output directory and checkpoints were stored and there were no errors.

Following the training, i ran evaluate_cnn.py using this command. 
```
python evaluate_cnn.py <path_to_test.source> test_generations.txt <model-name>  --score_path rouge_scores.txt
```
specified [here](https://github.com/huggingface/transformers/tree/master/examples/summarization). I made sure the above command points to test.source for the finetuning task.

However, the evaluation is unable to load the checkpoint and throws the following error
```
OSError: Can't load config for '/..PATH_TO../wiki_bart'. Make sure that:

- '/..PATH_TO../wiki_bart' is a correct model identifier listed on 'https://huggingface.co/models'

- or '/..PATH_TO../wiki_bart' is the correct path to a directory containing a config.json file
```
Seems like it is looking for the config.json which is not present along with the checkpoint files. 

I saw the finetune.py has a do_predict argument like do_train. Is that to be used instead of evaluate_cnn.py?

Can you please help?
Got around this by running the following code to generate the config.json and then running evaluate_cnn.py as above.

```
from lightning_base import BaseTransformer
from finetune import SummarizationTrainer
import torch
from argparse import Namespace

args = Namespace(adam_epsilon=1e-08, cache_dir='', config_name='', data_dir='PATH_TO_DATA', do_predict=False, do_train=False, eval_batch_size=4, learning_rate=3e-05, max_source_length=400, max_target_length=400, model_name_or_path='facebook/bart-large', n_gpu=8, num_train_epochs=3, output_dir='PATH_TO_OUTPUT', tokenizer_name='', train_batch_size=4, warmup_steps=0, weight_decay=0.0)

model = SummarizationTrainer(args)
model = model.load_from_checkpoint('PATH_TO_CHECKPOINT')
torch.save(model.state_dict(), args.output_dir + '/pytorch_model.bin')
model.config.to_json_file(args.output_dir + '/config.json')
```