add a bit more info to model card

README.md

@@ -5,10 +5,10 @@ license: mit
 thumbnail: https://en.wikipedia.org/wiki/Bart_Simpson#/media/File:Bart_Simpson_200px.png
 ---
 # BART for Gigaword
- - This model was created by fine-tuning the facebook/bart-large-cnn weights (also on HuggingFace) for the Gigaword dataset. The model was fine-tuned on the Gigaword training set for 3 epochs, and the model with the highest ROUGE-1 score on the training set batches was kept.
+ - This model was created by fine-tuning the `facebook/bart-large-cnn` weights (also on HuggingFace) for the Gigaword dataset. The model was fine-tuned on the Gigaword training set for 3 epochs, and the model with the highest ROUGE-1 score on the training set batches was kept.
  - The BART Tokenizer for CNN-Dailymail was used in the fine-tuning process and that is the tokenizer that will be loaded automatically when doing:
 ```
 from transformers import AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained("a1noack/bart-large-gigaword")
 ```
- - This model achieves ROUGE-1 / ROUGE-2 / ROUGE-L of 37.28 / 18.58 / 34.53 on the Gigaword test set; this is pretty good when compared to the PEGASUS results of 39.74 / 20.52 / 36.93.
+ - This model achieves ROUGE-1 / ROUGE-2 / ROUGE-L of 37.28 / 18.58 / 34.53 on the Gigaword test set; this is pretty good when compared to PEGASUS results of 39.12 / 19.86 / 36.24.