|  | @ -103,9 +103,8 @@ class AutoTransformers(NNOperator): | 
		
	
		
			
				|  |  |     def __call__(self, txt: str, return_sentence_emb: bool = False) -> numpy.ndarray: |  |  |     def __call__(self, txt: str, return_sentence_emb: bool = False) -> numpy.ndarray: | 
		
	
		
			
				|  |  |         try: |  |  |         try: | 
		
	
		
			
				|  |  |             inputs = self.tokenizer(txt, padding=True, truncation=True, return_tensors="pt").to(self.device) |  |  |             inputs = self.tokenizer(txt, padding=True, truncation=True, return_tensors="pt").to(self.device) | 
		
	
		
			
				|  |  |         except Exception as e: |  |  |  | 
		
	
		
			
				|  |  |             log.error(f'Invalid input for the tokenizer: {self.model_name}') |  |  |  | 
		
	
		
			
				|  |  |             raise e |  |  |  | 
		
	
		
			
				|  |  |  |  |  |         except Exception: | 
		
	
		
			
				|  |  |  |  |  |             inputs = self.tokenizer(dummy_input, truncation=True, return_tensors='pt').to(self.device) | 
		
	
		
			
				|  |  |         try: |  |  |         try: | 
		
	
		
			
				|  |  |             outs = self.model(**inputs) |  |  |             outs = self.model(**inputs) | 
		
	
		
			
				|  |  |         except Exception as e: |  |  |         except Exception as e: | 
		
	
	
		
			
				|  | @ -144,7 +143,10 @@ class AutoTransformers(NNOperator): | 
		
	
		
			
				|  |  |                 raise AttributeError('Unsupported model_type.') |  |  |                 raise AttributeError('Unsupported model_type.') | 
		
	
		
			
				|  |  | 
 |  |  | 
 | 
		
	
		
			
				|  |  |         dummy_input = '[CLS]' |  |  |         dummy_input = '[CLS]' | 
		
	
		
			
				|  |  |  |  |  |         try: | 
		
	
		
			
				|  |  |             inputs = self.tokenizer(dummy_input, padding=True, truncation=True, return_tensors='pt')  # a dictionary |  |  |             inputs = self.tokenizer(dummy_input, padding=True, truncation=True, return_tensors='pt')  # a dictionary | 
		
	
		
			
				|  |  |  |  |  |         except Exception: | 
		
	
		
			
				|  |  |  |  |  |             inputs = self.tokenizer(dummy_input, truncation=True, return_tensors='pt') | 
		
	
		
			
				|  |  |         if model_type == 'pytorch': |  |  |         if model_type == 'pytorch': | 
		
	
		
			
				|  |  |             torch.save(self._model, output_file) |  |  |             torch.save(self._model, output_file) | 
		
	
		
			
				|  |  |         elif model_type == 'torchscript': |  |  |         elif model_type == 'torchscript': | 
		
	
	
		
			
				|  | 
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