Update library versions, change messages

app.py

@@ -181,7 +181,7 @@ with demo:
                     #     choices=[i.value.name for i in Precision if i != Precision.Unknown],
                     #     label="Precision",
                     #     multiselect=False,
-                    #     value="float16",
+                    #     value="bfloat16",
                     #     interactive=True,
                     # )
                     # weight_type = gr.Dropdown(

requirements.txt

@@ -12,5 +12,5 @@ pandas
 python-dateutil
 tqdm
 transformers
-tokenizers>=0.20.0
+tokenizers>=0.15.0
 sentencepiece

src/submission/check_validity.py

@@ -34,33 +34,52 @@ def check_model_card(repo_id: str) -> tuple[bool, str]:
 
     return True, ""
 
-
-def is_model_on_hub(model_name: str, revision: str, token: str = None, trust_remote_code=False, test_tokenizer=False) -> tuple[bool, str]:
+def is_model_on_hub(
+    model_name: str,
+    revision: str,
+    token: str = None,
+    trust_remote_code: bool = False,
+    test_tokenizer: bool = False,
+) -> tuple[bool, str | None, AutoConfig | None]:
     """Checks if the model model_name is on the hub, and whether it (and its tokenizer) can be loaded with AutoClasses."""
-    try:
-        config = AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
+    try:     
+        config = AutoConfig.from_pretrained(
+            model_name,
+            revision=revision,
+            trust_remote_code=trust_remote_code,
+            token=token,
+        )
         if test_tokenizer:
             try:
-                _ = AutoTokenizer.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
+                _ = AutoTokenizer.from_pretrained(
+                    model_name,
+                    revision=revision,
+                    trust_remote_code=trust_remote_code,
+                    token=token,
+                )
             except ValueError as e:
                 return (
                     False,
-                    f"uses a tokenizer which is not in a transformers release: {e}",
-                    None
+                    'uses a tokenizer which is not in a transformers release: {}'.format(e),
+                    None,
                 )
             except Exception:
-                return (False, "'s tokenizer cannot be loaded. Is your tokenizer class in a stable transformers release, and correctly configured?", None)
+                return (
+                    False,
+                    "'s tokenizer cannot be loaded. Is your tokenizer class in a stable transformers release, and correctly configured?",
+                    None,
+                )
         return True, None, config
 
     except ValueError:
         return (
             False,
-            "needs to be launched with `trust_remote_code=True`. For safety reason, we do not allow these models to be automatically submitted to the leaderboard.",
-            None
+            'needs to be launched with `trust_remote_code=True`. For safety reason, we do not allow these models to be automatically submitted to the leaderboard.',
+            None,
         )
 
     except Exception:
-        return False, "was not found on hub!", None
+        return False, 'was not found.', None
 
 
 def get_model_size(model_info: ModelInfo, precision: str = None):

src/submission/submit.py

@@ -36,7 +36,7 @@ def add_new_eval(
     current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
 
     if model_type is None or model_type == "":
-        return styled_error("Please select a model type.")
+        return styled_error("Please, select a model type.")
 
     # Does the model actually exist?
     if revision == "":
@@ -57,7 +57,7 @@ def add_new_eval(
     try:
         model_info = API.model_info(repo_id=model_name, revision=revision)
     except Exception:
-        return styled_error("Could not get your model information. Please fill it up properly.")
+        return styled_error("Could not get your model information. Please, fill it up properly.")
 
     model_size = get_model_size(
         model_info=model_info,
@@ -68,14 +68,14 @@ def add_new_eval(
     try:
         license_title = model_info.cardData["license"]
     except Exception:
-        return styled_error("Please select a license for your model")
+        return styled_error("Please, select a license for your model.")
 
     is_model_card_ok, error_msg = check_model_card(model_name)
     if not is_model_card_ok:
         return styled_error(error_msg)
 
     # Seems good, creating the eval
-    print("Adding new eval")
+    print("Adding new evaluation request...")
 
     eval_entry = {
         # "model": model,
@@ -95,10 +95,10 @@ def add_new_eval(
     # Check for duplicate submission
     request_id = get_request_id(model_name, revision, precision)
     if request_id in REQUESTED_MODELS:
-        return styled_warning("This model has been already submitted.")
+        return styled_warning('This model has already been submitted.')
     request_hash = get_request_hash(model_name, revision, precision)
 
-    print("Creating eval file")
+    print("Creating evaluation request file...")
     OUT_DIR = f"{EVAL_REQUESTS_PATH}/{model_name}"
     os.makedirs(OUT_DIR, exist_ok=True)
 
@@ -109,13 +109,13 @@ def add_new_eval(
     with open(out_path, "w") as f:
         f.write(json.dumps(eval_entry))
 
-    print("Uploading eval file")
+    print("Uploading evaluation file...")
     API.upload_file(
         path_or_fileobj=out_path,
         path_in_repo='{}/{}.json'.format(model_name, request_hash),
         repo_id=REQUESTS_REPO,
         repo_type="dataset",
-        commit_message=f"Add {model_name} to eval requests",
+        commit_message=f"Add an evaluation request for {model_name}",
     )
 
     # Remove the local file