[RoBERTa] Minor clarifications to model doc (#31949)

* minor edits and clarifications

* address comment

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

---------

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

docs/source/en/model_doc/roberta.md

@@ -51,19 +51,19 @@ This model was contributed by [julien-c](https://huggingface.co/julien-c). The o
 
 ## Usage tips
 
-- This implementation is the same as [`BertModel`] with a tiny embeddings tweak as well as a setup
-  for Roberta pretrained models.
-- RoBERTa has the same architecture as BERT, but uses a byte-level BPE as a tokenizer (same as GPT-2) and uses a
+- This implementation is the same as [`BertModel`] with a minor tweak to the embeddings, as well as a setup
+  for RoBERTa pretrained models.
+- RoBERTa has the same architecture as BERT but uses a byte-level BPE as a tokenizer (same as GPT-2) and uses a
   different pretraining scheme.
-- RoBERTa doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just
-  separate your segments with the separation token `tokenizer.sep_token` (or `</s>`)
-- Same as BERT with better pretraining tricks:
-
-    * dynamic masking: tokens are masked differently at each epoch, whereas BERT does it once and for all
-    * together to reach 512 tokens (so the sentences are in an order than may span several documents)
-    * train with larger batches
-    * use BPE with bytes as a subunit and not characters (because of unicode characters)
-- [CamemBERT](camembert) is a wrapper around RoBERTa. Refer to this page for usage examples.
+- RoBERTa doesn't have `token_type_ids`, so you don't need to indicate which token belongs to which segment. Just
+  separate your segments with the separation token `tokenizer.sep_token` (or `</s>`).
+- RoBERTa is similar to BERT but with better pretraining techniques:
+
+    * Dynamic masking: tokens are masked differently at each epoch, whereas BERT does it once and for all.
+    * Sentence packing: Sentences are packed together to reach 512 tokens (so the sentences are in an order that may span several documents).
+    * Larger batches: Training uses larger batches.
+    * Byte-level BPE vocabulary: Uses BPE with bytes as a subunit instead of characters, accommodating Unicode characters.
+- [CamemBERT](camembert) is a wrapper around RoBERTa. Refer to its model page for usage examples.
 
 ## Resources