fixing typos

aixplain/enums/__init__.py

@@ -20,4 +20,4 @@
 from .asset_status import AssetStatus
 from .index_stores import IndexStores
 from .function_type import FunctionType
-from .code_interpeter import CodeInterpreterModel
+from .code_interpreter import CodeInterpreterModel

aixplain/factories/benchmark_factory.py

@@ -187,7 +187,7 @@ def _reformat_model_list(cls, model_list: List[Model]) -> Tuple[List[Any], List[
                 model_list_without_parms.append(model.id)
         if len(model_list_with_parms) > 0:
             if len(model_list_without_parms) > 0:
-                raise Exception("Please provide addditional info for all models or for none of the models")
+                raise Exception("Please provide additional info for all models or for none of the models")
         else:
             model_list_with_parms = None
         return model_list_without_parms, model_list_with_parms
@@ -196,7 +196,7 @@ def _reformat_model_list(cls, model_list: List[Model]) -> Tuple[List[Any], List[
     @classmethod
     def create(cls, name: str, dataset_list: List[Dataset], model_list: List[Model], metric_list: List[Metric]) -> Benchmark:
         """Creates a benchmark based on the information provided like name, dataset list, model list and score list.
-        Note: This only creates a benchmark. It needs to run seperately using start_benchmark_job.
+        Note: This only creates a benchmark. It needs to run separately using start_benchmark_job.
 
         Args:
             name (str): Unique Name of benchmark

aixplain/modules/agent/tool/custom_python_code_tool.py

@@ -25,7 +25,7 @@
 from aixplain.modules.agent.tool import Tool
 import logging
 from aixplain.enums import AssetStatus
-from aixplain.enums.code_interpeter import CodeInterpreterModel
+from aixplain.enums.code_interpreter import CodeInterpreterModel
 
 
 class CustomPythonCodeTool(Tool):

aixplain/modules/benchmark_job.py

@@ -134,7 +134,7 @@ def get_scores(self, return_simplified=True, return_as_dataframe=True):
             logging.error(error_message, exc_info=True)
             raise Exception(error_message)
 
-    def get_failuire_rate(self, return_as_dataframe=True):
+    def get_failure_rate(self, return_as_dataframe=True):
         try:
             scores = self.get_scores(return_simplified=False)
             failure_rates = {}
@@ -143,19 +143,19 @@ def get_failuire_rate(self, return_as_dataframe=True):
                     failure_rates[model_id] = 0
                     continue
                 score_info = model_info["rawScores"][0]
-                num_succesful = score_info["count"]
+                num_successful = score_info["count"]
                 num_failed = score_info["failedSegmentsCount"]
-                failuire_rate = (num_failed * 100) / (num_succesful + num_failed)
-                failure_rates[model_id] = failuire_rate
+                failure_rate = (num_failed * 100) / (num_successful + num_failed)
+                failure_rates[model_id] = failure_rate
             if return_as_dataframe:
                 df = pd.DataFrame()
                 df["Model"] = list(failure_rates.keys())
-                df["Failuire Rate"] = list(failure_rates.values())
+                df["Failure Rate"] = list(failure_rates.values())
                 return df
             else:
                 return failure_rates
         except Exception as e:
-            error_message = f"Benchmark scores: Error in Getting benchmark failuire rate: {e}"
+            error_message = f"Benchmark scores: Error in Getting benchmark failure rate: {e}"
             logging.error(error_message, exc_info=True)
             raise Exception(error_message)
 

aixplain/modules/metric.py

@@ -26,7 +26,7 @@
 
 
 class Metric(Asset):
-    """Represents a metric to be computed on one or more peices of data. It is usually linked to a machine learning task.
+    """Represents a metric to be computed on one or more pieces of data. It is usually linked to a machine learning task.
 
     Attributes:
         id (Text): ID of the Metric