Upload Upstream: comit message: my best model

{{cookiecutter.repo_name}}/cli.py

@@ -37,24 +37,23 @@ def validate():
         for task in tasks:
             downsample_rate = upstream.get_downsample_rates(task)
             assert isinstance(downsample_rate, int)
-            print(f"The upstream's representation for {task}"
-                f" has the downsample rate of {downsample_rate}.")
+
     except:
         print("Please check the Upstream Specification on https://superbbenchmark.org/challenge")
         raise
 
     typer.echo("All submission files validated!")
-    typer.echo("Now you can make a submission.")
+    typer.echo("Now you can upload these files to huggingface's Hub.")
 
 
 @app.command()
-def submit(submission_name: str):
+def upload(submission_name: str):
     subprocess.call("git pull origin main".split())
     subprocess.call(["git", "add", "."])
-    subprocess.call(["git", "commit", "-m", f"Submission: {submission_name} "])
+    subprocess.call(["git", "commit", "-m", f"Upload Upstream: {submission_name} "])
     subprocess.call(["git", "push"])
-    typer.echo("Submission successful!")
-
+    typer.echo("Upload successful!")
+    typer.echo("Now, please go to https://superbbenchmark.org/submit to make a submission.")
 
 if __name__ == "__main__":
     app()

{{cookiecutter.repo_name}}/expert.py

@@ -25,7 +25,7 @@ class Model(nn.Module):
         return [hidden, feature]
 
 class UpstreamExpert(nn.Module):
-    def __init__(self, ckpt: str = "model.pt", **kwargs):
+    def __init__(self, ckpt: str = "./model.pt", **kwargs):
         """
         Args:
             ckpt:
@@ -35,7 +35,7 @@ class UpstreamExpert(nn.Module):
         super().__init__()
         self.name = "[Example UpstreamExpert]"
 
-        print(f"{self.name} - You can use ckpt to load your pretrained weights: {ckpt}")
+        # You can use ckpt to load your pretrained weights
         ckpt = torch.load(ckpt, map_location="cpu")
         self.model = Model()
         self.model.load_state_dict(ckpt)