trainable = False bert_output = bert_model. from_pretrained ( "bert-base-uncased" ) # Freeze the BERT model to reuse the pretrained features without modifying them. int32, name = "token_type_ids" ) # Loading pretrained BERT model. Input ( shape = ( max_length ,), dtype = tf. int32, name = "attention_masks" ) # Token type ids are binary masks identifying different sequences in the model. int32, name = "input_ids" ) # Attention masks indicates to the model which tokens should be attended to. scope (): # Encoded token ids from BERT tokenizer. # Create the model under a distribution strategy scope. labels, dtype = "int32" ) return, labels else : return def on_epoch_end ( self ): # Shuffle indexes after each epoch if shuffle is set to True.
Similarity tester generator#
array ( encoded, dtype = "int32" ) # Set to true if data generator is used for training/validation. array ( encoded, dtype = "int32" ) token_type_ids = np. array ( encoded, dtype = "int32" ) attention_masks = np. tolist (), add_special_tokens = True, max_length = max_length, return_attention_mask = True, return_token_type_ids = True, pad_to_max_length = True, return_tensors = "tf", ) # Convert batch of encoded features to numpy array. sentence_pairs # With BERT tokenizer's batch_encode_plus batch of both the sentences are # encoded together and separated by token. batch_size def _getitem_ ( self, idx ): # Retrieves the batch of index. on_epoch_end () def _len_ ( self ): # Denotes the number of batches per epoch. from_pretrained ( "bert-base-uncased", do_lower_case = True ) self. # We will use base-base-uncased pretrained model. include_targets = include_targets # Load our BERT Tokenizer to encode the text. Returns: Tuples `(, labels)` (or just `` if `include_targets=False`) """ def _init_ ( self, sentence_pairs, labels, batch_size = batch_size, shuffle = True, include_targets = True, ): self. include_targets: boolean, whether to incude the labels. shuffle: boolean, whether to shuffle the data. Args: sentence_pairs: Array of premise and hypothesis input sentences. Sequence ): """Generates batches of data.
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