(self, default=True)
return self.response_df
Use spaCy NER to return named entities from text.
        :
        :default=True uses the pre-trained English language models provided by spaCy. 
        :default=False allows the use of a re-trained spaCy model.
Use spaCy NER to return named entities from text.
        :
        :default=True uses the pre-trained English language models provided by spaCy. 
        :default=False allows the use of a re-trained spaCy model.
Use spaCy NER to return named entities from text . : : default = True uses the pre - trained English language models provided by spaCy . : default = False allows the use of a re - trained spaCy model .
def get_entities(self, default=True):
        """
        Use spaCy NER to return named entities from text.
        :
        :default=True uses the pre-trained English language models provided by spaCy. 
        :default=False allows the use of a re-trained spaCy model.
        """

        if default:
            # Interpret the request data based on the expected row and column structure
            row_template = ['strData', 'strData', 'strData']
            col_headers = ['key', 'text', 'kwargs']
        else:
            # A model name is required if using a custom spaCy model
            row_template = ['strData', 'strData', 'strData', 'strData']
            col_headers = ['key', 'text', 'model_name', 'kwargs']
        
        # Create a Pandas DataFrame for the request data
        self.request_df = utils.request_df(self.request, row_template, col_headers)

        # Get the argument strings from the request dataframe
        kwargs = self.request_df.loc[0, 'kwargs']
        # Set the relevant parameters using the argument strings
        self._set_params(kwargs)

        # Print the request dataframe to the logs
        if self.debug:
            self._print_log(3)

        # Extract named entities for each text in the request dataframe
        self.response_df = self._entity_tagger()

        # Print the response dataframe to the logs
        if self.debug:
            self._print_log(4)

        # Send the reponse table description to Qlik
        self._send_table_description("entities")
        
        return self.response_df
def get_entities ( self , default = True ) : if default : # Interpret the request data based on the expected row and column structure row_template = [ 'strData' , 'strData' , 'strData' ] col_headers = [ 'key' , 'text' , 'kwargs' ] else : # A model name is required if using a custom spaCy model row_template = [ 'strData' , 'strData' , 'strData' , 'strData' ] col_headers = [ 'key' , 'text' , 'model_name' , 'kwargs' ] # Create a Pandas DataFrame for the request data self . request_df = utils . request_df ( self . request , row_template , col_headers ) # Get the argument strings from the request dataframe kwargs = self . request_df . loc [ 0 , 'kwargs' ] # Set the relevant parameters using the argument strings self . _set_params ( kwargs ) # Print the request dataframe to the logs if self . debug : self . _print_log ( 3 ) # Extract named entities for each text in the request dataframe self . response_df = self . _entity_tagger ( ) # Print the response dataframe to the logs if self . debug : self . _print_log ( 4 ) # Send the reponse table description to Qlik self . _send_table_description ( "entities" ) return self . response_df