lang

src/backend/manage_requests.py

@@ -22,7 +22,8 @@ class EvalRequest:
     likes: Optional[int] = 0
     params: Optional[int] = None
     license: Optional[str] = ""
-    
+    lang: Optional[str] = ""
+
     def get_model_args(self):
         model_args = f"pretrained={self.model},revision={self.revision}"
 

src/display/utils.py

@@ -34,11 +34,12 @@ auto_eval_column_dict.append(["average_mc", ColumnContent, ColumnContent("Avg mc
 for task in Tasks:
     auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
 # Model information
-auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", True)])
+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(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
 auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)])
 auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
+auto_eval_column_dict.append(["lang", ColumnContent, ColumnContent("Lang", "str", True)])
 auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", True)])
 auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)])
 auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
@@ -72,6 +73,7 @@ class ModelType(Enum):
     CPT = ModelDetails(name="continuously pretrained", symbol="🟩")
     IFT = ModelDetails(name="instruction-tuned", symbol="⭕")
     RL = ModelDetails(name="RL-tuned", symbol="💬")
+    Baseline = ModelDetails(name="baseline", symbol="⚖")
     Unknown = ModelDetails(name="", symbol="?")
 
     def to_str(self, separator=" "):
@@ -87,6 +89,8 @@ class ModelType(Enum):
             return ModelType.RL
         if "instruction-tuned" in type or "⭕" in type:
             return ModelType.IFT
+        if "baseline" in type or "⚖" in type:
+            return ModelType.IFT
         return ModelType.Unknown
 
 class WeightType(Enum):

src/leaderboard/read_evals.py

@@ -27,6 +27,7 @@ class EvalResult:
     weight_type: WeightType = WeightType.Original # Original or Adapter
     architecture: str = "Unknown" 
     license: str = "?"
+    lang: str = "?"
     likes: int = 0
     num_params: int = 0
     date: str = "" # submission date of request file
@@ -113,7 +114,7 @@ class EvalResult:
             self.model_type = ModelType.from_str(meta.get("type", "?"))
             self.num_params = meta.get("params", 0)
             self.license = meta.get("license", "?")
-            # self.lang = meta.get("lang", "?") #TODO
+            self.lang = meta.get("lang", "?")
             #TODO desc name
         except KeyError:
             print(f"Could not find metadata for {self.full_model}")
@@ -239,6 +240,13 @@ class EvalResult:
         except AttributeError:
             print(f"AttributeError license")
 
+        try:
+            data_dict[AutoEvalColumn.lang.name] = self.lang
+        except KeyError:
+            print(f"Could not find lang")
+        except AttributeError:
+            print(f"AttributeError lang")
+
         try:
             data_dict[AutoEvalColumn.likes.name] = self.likes
         except KeyError: