License

src/display/utils.py

@@ -37,8 +37,6 @@ for task in Tasks:
 # Model information
 auto_eval_column_dict.append(
     ["model_type", ColumnContent, ColumnContent("Type", "str", False)])
-auto_eval_column_dict.append(
-    ["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
 auto_eval_column_dict.append(
     ["precision", ColumnContent, ColumnContent("Precision", "str", False)])
 auto_eval_column_dict.append(

src/leaderboard/read_evals.py

@@ -100,7 +100,6 @@ class EvalResult:
             with open(request_file, "r") as f:
                 request = json.load(f)
             self.model_type = ModelType.from_str(request.get("model_type", ""))
-            self.license = request.get("license", "?")
             self.likes = request.get("likes", 0)
             self.num_params = request.get("params", 0)
             self.date = request.get("submitted_time", "")
@@ -117,14 +116,10 @@ class EvalResult:
             AutoEvalColumn.precision.name: self.precision.value.name,
             AutoEvalColumn.model_type.name: self.model_type.value.name,
             AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol,
-            AutoEvalColumn.architecture.name: self.architecture,
             AutoEvalColumn.model.name: make_clickable_model(self.full_model),
             AutoEvalColumn.revision.name: self.revision,
             # AutoEvalColumn.average.name: average,
-            AutoEvalColumn.license.name: self.license,
-            AutoEvalColumn.likes.name: self.likes,
             AutoEvalColumn.params.name: self.num_params,
-            AutoEvalColumn.still_on_hub.name: self.still_on_hub,
         }
 
         for task in Tasks:

src/submission/check_validity.py

@@ -10,34 +10,30 @@ from huggingface_hub.hf_api import ModelInfo
 from transformers import AutoConfig
 from transformers.models.auto.tokenization_auto import AutoTokenizer
 
+
 def check_model_card(repo_id: str) -> tuple[bool, str]:
-    """Checks if the model card and license exist and have been filled"""
+    """Checks if the model card exist and have been filled"""
     try:
         card = ModelCard.load(repo_id)
     except huggingface_hub.utils.EntryNotFoundError:
         return False, "Please add a model card to your model to explain how you trained/fine-tuned it."
 
-    # Enforce license metadata
-    if card.data.license is None:
-        if not ("license_name" in card.data and "license_link" in card.data):
-            return False, (
-                "License not found. Please add a license to your model card using the `license` metadata or a"
-                " `license_name`/`license_link` pair."
-            )
-
     # Enforce card content
     if len(card.text) < 200:
         return False, "Please add a description to your model card, it is too short."
 
     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]:
     """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)
+        config = AutoConfig.from_pretrained(
+            model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
         if test_tokenizer:
             try:
-                tk = AutoTokenizer.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
+                tk = AutoTokenizer.from_pretrained(
+                    model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
             except ValueError as e:
                 return (
                     False,
@@ -66,14 +62,17 @@ def get_model_size(model_info: ModelInfo, precision: str):
     except (AttributeError, TypeError):
         return 0  # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py
 
-    size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1
+    size_factor = 8 if (
+        precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1
     model_size = size_factor * model_size
     return model_size
 
+
 def get_model_arch(model_info: ModelInfo):
     """Gets the model architecture from the configuration"""
     return model_info.config.get("architectures", "Unknown")
 
+
 def already_submitted_models(requested_models_dir: str) -> set[str]:
     """Gather a list of already submitted models to avoid duplicates"""
     depth = 1
@@ -88,12 +87,14 @@ def already_submitted_models(requested_models_dir: str) -> set[str]:
                     continue
                 with open(os.path.join(root, file), "r") as f:
                     info = json.load(f)
-                    file_names.append(f"{info['model']}_{info['revision']}_{info['precision']}")
+                    file_names.append(
+                        f"{info['model']}_{info['revision']}_{info['precision']}")
 
                     # Select organisation
                     if info["model"].count("/") == 0 or "submitted_time" not in info:
                         continue
                     organisation, _ = info["model"].split("/")
-                    users_to_submission_dates[organisation].append(info["submitted_time"])
+                    users_to_submission_dates[organisation].append(
+                        info["submitted_time"])
 
     return set(file_names), users_to_submission_dates