bert_intent_prediction_bert_layer.py

class BertModel(object):
    
    def __init__(self):
        
        self.max_len = 128
        bert_path = "https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/1"
        FullTokenizer=bert.bert_tokenization.FullTokenizer
        
        self.bert_module = hub.KerasLayer(bert_path,trainable=True)

        self.vocab_file = self.bert_module.resolved_object.vocab_file.asset_path.numpy()

        self.do_lower_case = self.bert_module.resolved_object.do_lower_case.numpy()

        self.tokenizer = FullTokenizer(self.vocab_file,self.do_lower_case)
        
    def get_masks(self,tokens, max_seq_length):
        mask_data =  [1]*len(tokens) + [0] * (max_seq_length - len(tokens))
        return mask_data

    def get_segments(self,tokens, max_seq_length):
        '''
        Segments: 0 for the first sequence, 
        1 for the second
        '''
        segments = []
        segment_id = 0
        for token in tokens:
            segments.append(current_segment_id)
            if token == "[SEP]":
                segment_id = 1
        '''Remaining are padded with 0'''
        remaining_segment = [0] * (max_seq_length - len(tokens))
        segment_data = segments + remaining_segment
        return segment_data
    
    def get_ids(self,tokens, tokenizer, max_seq_length):
        token_ids = tokenizer.convert_tokens_to_ids(tokens,)
        remaining_ids = [0] * (max_seq_length-len(token_ids))
        input_ids = token_ids + remaining_ids
        return input_ids
    
    def get_input_data(self,sentence,maxlen):

        sent_token = self.tokenizer.tokenize(sentence)

        sent_token = sent_token[:maxlen]

        sent_token = ["[CLS]"] + sent_token + ["[SEP]"]

        id = self.get_ids(sent_token, self.tokenizer, self.max_len)
        mask = self.get_masks(sent_token, self.max_len)
        segment = self.get_segments(sent_token, self.max_len)
        input_data = [id,mask,segment]
        return input_data

    def get_input_array(self,sentences):
        
        input_ids, input_masks, input_segments = [], [], []

        for sentence in tqdm(sentences,position=0, leave=True):
            ids,masks,segments=self.get_input_data(sentence,self.max_len-2)

            input_ids.append(ids)
            input_masks.append(masks)
            input_segments.append(segments)
            
        input_array = [np.asarray(input_ids, dtype=np.int32),np.asarray(input_masks, dtype=np.int32), np.asarray(input_segments, dtype=np.int32)]
        return input_array