replace use_fast_tokenizer with tokenizer_args

sentence_transformers/cross_encoder/CrossEncoder.py

@@ -15,7 +15,7 @@
 
 
 class CrossEncoder():
-    def __init__(self, model_name:str, num_labels:int = None, max_length:int = None, device:str = None, use_fast_tokenizer:bool = None):
+    def __init__(self, model_name:str, num_labels:int = None, max_length:int = None, device:str = None, tokenizer_args:Dict = {}):
         """
         A CrossEncoder takes exactly two sentences / texts as input and either predicts
         a score or label for this sentence pair. It can for example predict the similarity of the sentence pair
@@ -27,7 +27,7 @@ def __init__(self, model_name:str, num_labels:int = None, max_length:int = None,
         :param num_labels: Number of labels of the classifier. If 1, the CrossEncoder is a regression model that outputs a continous score 0...1. If > 1, it output several scores that can be soft-maxed to get probability scores for the different classes.
         :param max_length: Max length for input sequences. Longer sequences will be truncated. If None, max length of the model will be used
         :param device: Device that should be used for the model. If None, it will use CUDA if available.
-        :param use_fast_tokenizer: Use fast tokenizer from hugging face.
+        :param tokenizer_args: Arguments passed to AutoTokenizer
         """
 
         self.config = AutoConfig.from_pretrained(model_name)
@@ -42,10 +42,6 @@ def __init__(self, model_name:str, num_labels:int = None, max_length:int = None,
             self.config.num_labels = num_labels
 
         self.model = AutoModelForSequenceClassification.from_pretrained(model_name, config=self.config)
-        tokenizer_args = {}
-        if use_fast_tokenizer is not None:
-            tokenizer_args['use_fast'] = use_fast_tokenizer
-
         self.tokenizer = AutoTokenizer.from_pretrained(model_name, **tokenizer_args)
 
         self.max_length = max_length