Update all references to canonical models (#29001)

* Script & Manual edition

* Update

examples/research_projects/bertabs/convert_bertabs_original_pytorch_checkpoint.py

@@ -107,7 +107,7 @@ def convert_bertabs_checkpoints(path_to_checkpoints, dump_path):
     # ----------------------------------
 
     logging.info("Make sure that the models' outputs are identical")
-    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+    tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
 
     # prepare the model inputs
     encoder_input_ids = tokenizer.encode("This is sample éàalj'-.")

examples/research_projects/bertabs/modeling_bertabs.py

@@ -128,7 +128,7 @@ class Bert(nn.Module):
 
     def __init__(self):
         super().__init__()
-        config = BertConfig.from_pretrained("bert-base-uncased")
+        config = BertConfig.from_pretrained("google-bert/bert-base-uncased")
         self.model = BertModel(config)
 
     def forward(self, input_ids, attention_mask=None, token_type_ids=None, **kwargs):

examples/research_projects/bertabs/run_summarization.py

@@ -29,7 +29,7 @@ Batch = namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mas
 
 
 def evaluate(args):
-    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased", do_lower_case=True)
+    tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased", do_lower_case=True)
     model = BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization")
     model.to(args.device)
     model.eval()