Update all references to canonical models (#29001)

* Script & Manual edition

* Update

examples/pytorch/summarization/README.md

@@ -41,7 +41,7 @@ and you also will find examples of these below.
 Here is an example on a summarization task:
 ```bash
 python examples/pytorch/summarization/run_summarization.py \
-    --model_name_or_path t5-small \
+    --model_name_or_path google-t5/t5-small \
     --do_train \
     --do_eval \
     --dataset_name cnn_dailymail \
@@ -54,9 +54,9 @@ python examples/pytorch/summarization/run_summarization.py \
     --predict_with_generate
 ```
 
-Only T5 models `t5-small`, `t5-base`, `t5-large`, `t5-3b` and `t5-11b` must use an additional argument: `--source_prefix "summarize: "`.
+Only T5 models `google-t5/t5-small`, `google-t5/t5-base`, `google-t5/t5-large`, `google-t5/t5-3b` and `google-t5/t5-11b` must use an additional argument: `--source_prefix "summarize: "`.
 
-We used CNN/DailyMail dataset in this example as `t5-small` was trained on it and one can get good scores even when pre-training with a very small sample.
+We used CNN/DailyMail dataset in this example as `google-t5/t5-small` was trained on it and one can get good scores even when pre-training with a very small sample.
 
 Extreme Summarization (XSum) Dataset is another commonly used dataset for the task of summarization. To use it replace `--dataset_name cnn_dailymail --dataset_config "3.0.0"` with  `--dataset_name xsum`.
 
@@ -65,7 +65,7 @@ And here is how you would use it on your own files, after adjusting the values f
 
 ```bash
 python examples/pytorch/summarization/run_summarization.py \
-    --model_name_or_path t5-small \
+    --model_name_or_path google-t5/t5-small \
     --do_train \
     --do_eval \
     --train_file path_to_csv_or_jsonlines_file \
@@ -156,7 +156,7 @@ then
 
 ```bash
 python run_summarization_no_trainer.py \
-    --model_name_or_path t5-small \
+    --model_name_or_path google-t5/t5-small \
     --dataset_name cnn_dailymail \
     --dataset_config "3.0.0" \
     --source_prefix "summarize: " \
@@ -179,7 +179,7 @@ that will check everything is ready for training. Finally, you can launch traini
 
 ```bash
 accelerate launch run_summarization_no_trainer.py \
-    --model_name_or_path t5-small \
+    --model_name_or_path google-t5/t5-small \
     --dataset_name cnn_dailymail \
     --dataset_config "3.0.0" \
     --source_prefix "summarize: " \

examples/pytorch/summarization/run_summarization.py

@@ -368,11 +368,11 @@ def main():
     logger.info(f"Training/evaluation parameters {training_args}")
 
     if data_args.source_prefix is None and model_args.model_name_or_path in [
-        "t5-small",
-        "t5-base",
-        "t5-large",
-        "t5-3b",
-        "t5-11b",
+        "google-t5/t5-small",
+        "google-t5/t5-base",
+        "google-t5/t5-large",
+        "google-t5/t5-3b",
+        "google-t5/t5-11b",
     ]:
         logger.warning(
             "You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with "

examples/pytorch/summarization/run_summarization_no_trainer.py

@@ -339,11 +339,11 @@ def main():
 
     accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)
     if args.source_prefix is None and args.model_name_or_path in [
-        "t5-small",
-        "t5-base",
-        "t5-large",
-        "t5-3b",
-        "t5-11b",
+        "google-t5/t5-small",
+        "google-t5/t5-base",
+        "google-t5/t5-large",
+        "google-t5/t5-3b",
+        "google-t5/t5-11b",
     ]:
         logger.warning(
             "You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with "