Update docstrings

Author: cdpierse

transformers_interpret/explainers/question_answering.py

@@ -371,7 +371,14 @@ def __call__(
             question (str): The question text
             text (str): The text or context from which the model finds an answers
             embedding_type (int, optional): The embedding type word(0), position(1), all(2) to calculate attributions for.
-            Defaults to 2.
+                Defaults to 2.
+            internal_batch_size (int, optional): Divides total #steps * #examples
+                data points into chunks of size at most internal_batch_size,
+                which are computed (forward / backward passes)
+                sequentially. If internal_batch_size is None, then all evaluations are
+                processed in one batch.
+            n_steps (int, optional): The number of steps used by the approximation
+                method. Default: 50.
 
         Returns:
             dict: Dict for start and end position word attributions.

transformers_interpret/explainers/sequence_classification.py

@@ -306,7 +306,13 @@ def __call__(
             index (int, optional): Optional output index to provide attributions for. Defaults to None.
             class_name (str, optional): Optional output class name to provide attributions for. Defaults to None.
             embedding_type (int, optional): The embedding type word(0) or position(1) to calculate attributions for. Defaults to 0.
-
+            internal_batch_size (int, optional): Divides total #steps * #examples
+                data points into chunks of size at most internal_batch_size,
+                which are computed (forward / backward passes)
+                sequentially. If internal_batch_size is None, then all evaluations are
+                processed in one batch.
+            n_steps (int, optional): The number of steps used by the approximation
+                method. Default: 50.
         Returns:
             list: List of tuples containing words and their associated attribution scores.
         """

transformers_interpret/explainers/zero_shot_classification.py

@@ -292,7 +292,13 @@ def __call__(
                 Defaults to "this text is about {} .".
             include_hypothesis (bool, optional): Alternative option to include hypothesis text in attributions
                 and visualization. Defaults to False.
-
+            internal_batch_size (int, optional): Divides total #steps * #examples
+                data points into chunks of size at most internal_batch_size,
+                which are computed (forward / backward passes)
+                sequentially. If internal_batch_size is None, then all evaluations are
+                processed in one batch.
+            n_steps (int, optional): The number of steps used by the approximation
+                method. Default: 50.
         Returns:
             list: List of tuples containing words and their associated attribution scores.
         """