From e08c01aa1ad63efff83548ea69d5ba3ce4a75acc Mon Sep 17 00:00:00 2001
From: LysandreJik <lysandre.debut@reseau.eseo.fr>
Date: Mon, 26 Aug 2019 18:13:06 -0400
Subject: [PATCH] fix #1102

---
 pytorch_transformers/modeling_roberta.py     | 6 +++---
 pytorch_transformers/tokenization_roberta.py | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/pytorch_transformers/modeling_roberta.py b/pytorch_transformers/modeling_roberta.py
index e49b2a06b1..cbd88ab86e 100644
--- a/pytorch_transformers/modeling_roberta.py
+++ b/pytorch_transformers/modeling_roberta.py
@@ -98,15 +98,15 @@ ROBERTA_INPUTS_DOCSTRING = r"""
     Inputs:
         **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
             Indices of input sequence tokens in the vocabulary.
-            To match pre-training, RoBERTa input sequence should be formatted with [CLS] and [SEP] tokens as follows:
+            To match pre-training, RoBERTa input sequence should be formatted with <s> and </s> tokens as follows:
 
             (a) For sequence pairs:
 
-                ``tokens:         [CLS] is this jack ##son ##ville ? [SEP][SEP] no it is not . [SEP]``
+                ``tokens:         <s> Is this Jacksonville ? </s> </s> No it is not . </s>``
 
             (b) For single sequences:
 
-                ``tokens:         [CLS] the dog is hairy . [SEP]``
+                ``tokens:         <s> the dog is hairy . </s>``
 
             Fully encoded sequences or sequence pairs can be obtained using the RobertaTokenizer.encode function with 
             the ``add_special_tokens`` parameter set to ``True``.
diff --git a/pytorch_transformers/tokenization_roberta.py b/pytorch_transformers/tokenization_roberta.py
index edf4717c89..13d963d432 100644
--- a/pytorch_transformers/tokenization_roberta.py
+++ b/pytorch_transformers/tokenization_roberta.py
@@ -163,14 +163,14 @@ class RobertaTokenizer(PreTrainedTokenizer):
     def add_special_tokens_single_sentence(self, token_ids):
         """
         Adds special tokens to a sequence for sequence classification tasks.
-        A RoBERTa sequence has the following format: [CLS] X [SEP]
+        A RoBERTa sequence has the following format: <s> X </s>
         """
         return [self._convert_token_to_id(self.cls_token)] + token_ids + [self._convert_token_to_id(self.sep_token)]
 
     def add_special_tokens_sentences_pair(self, token_ids_0, token_ids_1):
         """
         Adds special tokens to a sequence pair for sequence classification tasks.
-        A RoBERTa sequence pair has the following format: [CLS] A [SEP][SEP] B [SEP]
+        A RoBERTa sequence pair has the following format: <s> A </s></s> B </s>
         """
         sep = [self._convert_token_to_id(self.sep_token)]
         cls = [self._convert_token_to_id(self.cls_token)]