refer to Transformer instead

pyterrier/text.py

@@ -1,4 +1,5 @@
 from pyterrier.transformer import TransformerBase
+from . import Transformer
 from pyterrier.datasets import IRDSDataset
 import more_itertools
 from collections import defaultdict
@@ -248,16 +249,16 @@ def slidingWindow(sequence : list, winSize : int, step : int) -> list:
     return [x for x in list(more_itertools.windowed(sequence,n=winSize, step=step)) if x[-1] is not None]
 
 def snippets(
-        text_scorer_pipe : TransformerBase, 
+        text_scorer_pipe : Transformer, 
         text_attr : str = "text", 
         summary_attr : str = "summary", 
         num_psgs : int = 5, 
-        joinstr : str ='...') -> TransformerBase:
+        joinstr : str ='...') -> Transformer:
     """
     Applies query-biased summarisation (snippet), by applying the specified text scoring pipeline.
 
     Arguments:
-     - text_scorer_pipe(TransformerBase): the pipeline for scoring passages in response to the query. Normally this applies passaging.
+     - text_scorer_pipe(Transformer): the pipeline for scoring passages in response to the query. Normally this applies passaging.
      - text_attr(str): what is the name of the attribute that contains the text of the document
      - summary_attr(str): what is the name of the attribute that should contain the query-biased summary for that document
      - num_psgs(int): how many passages to select for the summary of each document