updated documentation with the Please ask your administrator. flag

README.md

@@ -64,8 +64,9 @@ Sem-F1 also accepts multiple optional arguments:
 - `tokenize_sentences (bool)`: Flag to indicate whether to tokenize the sentences in the input documents. Default: True.
 - `multi_references (bool)`: Flag to indicate whether multiple references are provided. Default: False.
 - `gpu (Union[bool, str, int, List[Union[str, int]]])`: Whether to use GPU, CPU or multiple-processes for computation.
-- `batch_size (int)`: Batch size for encoding. Default: 32.
-- `verbose (bool)`: Flag to indicate verbose output. Default: False.
+- `batch_size (int)`: Batch size for encoding. (Default: 32).
+- `verbose (bool)`: Flag to indicate verbose output. (Default: False).
+- `aggregate (bool)`: Flag which is used to automatically compute the mean precision, recall and f1 scores. (Default: False).
 
 Refer to the inputs descriptions for more detailed usage as follows:
 

semf1.py

@@ -83,9 +83,10 @@ Args:
         List[Union[str, int]] - Multiple GPUs/cpus i.e. use multiple processes when computing embeddings
     batch_size (int): Batch size for encoding. Default is 32.
     verbose (bool): Flag to indicate verbose output. Default is False.
+    aggregate (bool): Flag which is used to automatically compute the mean precision, recall and f1 scores. (Default: False)
 
 Returns:
-    List of Scores dataclass with attributes as follows - 
+    Singleton/List of Scores dataclass with attributes as follows - 
         precision: float - precision score
         recall: List[float] - List of recall scores corresponding to single/multiple references
         f1: float - F1 score (between precision and average recall)