Add model card creation snippet to example scripts (#13730)

* Update run_glue.py

* Update run_glue.py

* Add model creation snippet to other scripts

* Fix style

examples/pytorch/language-modeling/run_clm.py

@@ -500,17 +500,19 @@ def main():
         trainer.log_metrics("eval", metrics)
         trainer.save_metrics("eval", metrics)
 
-    if training_args.push_to_hub:
-        kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-generation"}
-        if data_args.dataset_name is not None:
-            kwargs["dataset_tags"] = data_args.dataset_name
-            if data_args.dataset_config_name is not None:
-                kwargs["dataset_args"] = data_args.dataset_config_name
-                kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
-            else:
-                kwargs["dataset"] = data_args.dataset_name
+    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-generation"}
+    if data_args.dataset_name is not None:
+        kwargs["dataset_tags"] = data_args.dataset_name
+        if data_args.dataset_config_name is not None:
+            kwargs["dataset_args"] = data_args.dataset_config_name
+            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+        else:
+            kwargs["dataset"] = data_args.dataset_name
 
+    if training_args.push_to_hub:
         trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
 
 
 def _mp_fn(index):

examples/pytorch/language-modeling/run_mlm.py

@@ -528,17 +528,19 @@ def main():
         trainer.log_metrics("eval", metrics)
         trainer.save_metrics("eval", metrics)
 
-    if training_args.push_to_hub:
-        kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "fill-mask"}
-        if data_args.dataset_name is not None:
-            kwargs["dataset_tags"] = data_args.dataset_name
-            if data_args.dataset_config_name is not None:
-                kwargs["dataset_args"] = data_args.dataset_config_name
-                kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
-            else:
-                kwargs["dataset"] = data_args.dataset_name
+    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "fill-mask"}
+    if data_args.dataset_name is not None:
+        kwargs["dataset_tags"] = data_args.dataset_name
+        if data_args.dataset_config_name is not None:
+            kwargs["dataset_args"] = data_args.dataset_config_name
+            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+        else:
+            kwargs["dataset"] = data_args.dataset_name
 
+    if training_args.push_to_hub:
         trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
 
 
 def _mp_fn(index):

examples/pytorch/language-modeling/run_plm.py

@@ -499,17 +499,19 @@ def main():
         trainer.log_metrics("eval", metrics)
         trainer.save_metrics("eval", metrics)
 
-    if training_args.push_to_hub:
-        kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "language-modeling"}
-        if data_args.dataset_name is not None:
-            kwargs["dataset_tags"] = data_args.dataset_name
-            if data_args.dataset_config_name is not None:
-                kwargs["dataset_args"] = data_args.dataset_config_name
-                kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
-            else:
-                kwargs["dataset"] = data_args.dataset_name
+    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "language-modeling"}
+    if data_args.dataset_name is not None:
+        kwargs["dataset_tags"] = data_args.dataset_name
+        if data_args.dataset_config_name is not None:
+            kwargs["dataset_args"] = data_args.dataset_config_name
+            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+        else:
+            kwargs["dataset"] = data_args.dataset_name
 
+    if training_args.push_to_hub:
         trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
 
 
 def _mp_fn(index):

examples/pytorch/multiple-choice/run_swag.py

@@ -430,15 +430,19 @@ def main():
         trainer.log_metrics("eval", metrics)
         trainer.save_metrics("eval", metrics)
 
+    kwargs = dict(
+        finetuned_from=model_args.model_name_or_path,
+        tasks="multiple-choice",
+        dataset_tags="swag",
+        dataset_args="regular",
+        dataset="SWAG",
+        language="en",
+    )
+
     if training_args.push_to_hub:
-        trainer.push_to_hub(
-            finetuned_from=model_args.model_name_or_path,
-            tasks="multiple-choice",
-            dataset_tags="swag",
-            dataset_args="regular",
-            dataset="SWAG",
-            language="en",
-        )
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
 
 
 def _mp_fn(index):

examples/pytorch/question-answering/run_qa.py

@@ -623,17 +623,19 @@ def main():
         trainer.log_metrics("predict", metrics)
         trainer.save_metrics("predict", metrics)
 
-    if training_args.push_to_hub:
-        kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "question-answering"}
-        if data_args.dataset_name is not None:
-            kwargs["dataset_tags"] = data_args.dataset_name
-            if data_args.dataset_config_name is not None:
-                kwargs["dataset_args"] = data_args.dataset_config_name
-                kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
-            else:
-                kwargs["dataset"] = data_args.dataset_name
+    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "question-answering"}
+    if data_args.dataset_name is not None:
+        kwargs["dataset_tags"] = data_args.dataset_name
+        if data_args.dataset_config_name is not None:
+            kwargs["dataset_args"] = data_args.dataset_config_name
+            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+        else:
+            kwargs["dataset"] = data_args.dataset_name
 
+    if training_args.push_to_hub:
         trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
 
 
 def _mp_fn(index):

examples/pytorch/question-answering/run_qa_beam_search.py

@@ -656,17 +656,19 @@ def main():
         trainer.log_metrics("predict", metrics)
         trainer.save_metrics("predict", metrics)
 
-    if training_args.push_to_hub:
-        kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "question-answering"}
-        if data_args.dataset_name is not None:
-            kwargs["dataset_tags"] = data_args.dataset_name
-            if data_args.dataset_config_name is not None:
-                kwargs["dataset_args"] = data_args.dataset_config_name
-                kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
-            else:
-                kwargs["dataset"] = data_args.dataset_name
+    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "question-answering"}
+    if data_args.dataset_name is not None:
+        kwargs["dataset_tags"] = data_args.dataset_name
+        if data_args.dataset_config_name is not None:
+            kwargs["dataset_args"] = data_args.dataset_config_name
+            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+        else:
+            kwargs["dataset"] = data_args.dataset_name
 
+    if training_args.push_to_hub:
         trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
 
 
 def _mp_fn(index):

examples/pytorch/summarization/run_summarization.py

@@ -622,17 +622,19 @@ def main():
                 with open(output_prediction_file, "w") as writer:
                     writer.write("\n".join(predictions))
 
-    if training_args.push_to_hub:
-        kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "summarization"}
-        if data_args.dataset_name is not None:
-            kwargs["dataset_tags"] = data_args.dataset_name
-            if data_args.dataset_config_name is not None:
-                kwargs["dataset_args"] = data_args.dataset_config_name
-                kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
-            else:
-                kwargs["dataset"] = data_args.dataset_name
+    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "summarization"}
+    if data_args.dataset_name is not None:
+        kwargs["dataset_tags"] = data_args.dataset_name
+        if data_args.dataset_config_name is not None:
+            kwargs["dataset_args"] = data_args.dataset_config_name
+            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+        else:
+            kwargs["dataset"] = data_args.dataset_name
 
+    if training_args.push_to_hub:
         trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
 
     return results
 

examples/pytorch/text-classification/run_glue.py

@@ -546,15 +546,17 @@ def main():
                             item = label_list[item]
                             writer.write(f"{index}\t{item}\n")
 
-    if training_args.push_to_hub:
-        kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}
-        if data_args.task_name is not None:
-            kwargs["language"] = "en"
-            kwargs["dataset_tags"] = "glue"
-            kwargs["dataset_args"] = data_args.task_name
-            kwargs["dataset"] = f"GLUE {data_args.task_name.upper()}"
+    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}
+    if data_args.task_name is not None:
+        kwargs["language"] = "en"
+        kwargs["dataset_tags"] = "glue"
+        kwargs["dataset_args"] = data_args.task_name
+        kwargs["dataset"] = f"GLUE {data_args.task_name.upper()}"
 
+    if training_args.push_to_hub:
         trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
 
 
 def _mp_fn(index):

examples/pytorch/token-classification/run_ner.py

@@ -542,17 +542,19 @@ def main():
                 for prediction in true_predictions:
                     writer.write(" ".join(prediction) + "\n")
 
-    if training_args.push_to_hub:
-        kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "token-classification"}
-        if data_args.dataset_name is not None:
-            kwargs["dataset_tags"] = data_args.dataset_name
-            if data_args.dataset_config_name is not None:
-                kwargs["dataset_args"] = data_args.dataset_config_name
-                kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
-            else:
-                kwargs["dataset"] = data_args.dataset_name
+    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "token-classification"}
+    if data_args.dataset_name is not None:
+        kwargs["dataset_tags"] = data_args.dataset_name
+        if data_args.dataset_config_name is not None:
+            kwargs["dataset_args"] = data_args.dataset_config_name
+            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+        else:
+            kwargs["dataset"] = data_args.dataset_name
 
+    if training_args.push_to_hub:
         trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
 
 
 def _mp_fn(index):

examples/pytorch/translation/run_translation.py

@@ -590,21 +590,23 @@ def main():
                 with open(output_prediction_file, "w", encoding="utf-8") as writer:
                     writer.write("\n".join(predictions))
 
-    if training_args.push_to_hub:
-        kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "translation"}
-        if data_args.dataset_name is not None:
-            kwargs["dataset_tags"] = data_args.dataset_name
-            if data_args.dataset_config_name is not None:
-                kwargs["dataset_args"] = data_args.dataset_config_name
-                kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
-            else:
-                kwargs["dataset"] = data_args.dataset_name
-
-        languages = [l for l in [data_args.source_lang, data_args.target_lang] if l is not None]
-        if len(languages) > 0:
-            kwargs["language"] = languages
+    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "translation"}
+    if data_args.dataset_name is not None:
+        kwargs["dataset_tags"] = data_args.dataset_name
+        if data_args.dataset_config_name is not None:
+            kwargs["dataset_args"] = data_args.dataset_config_name
+            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+        else:
+            kwargs["dataset"] = data_args.dataset_name
 
+    languages = [l for l in [data_args.source_lang, data_args.target_lang] if l is not None]
+    if len(languages) > 0:
+        kwargs["language"] = languages
+
+    if training_args.push_to_hub:
         trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
 
     return results