I am trying to feed the input and target sentences to an NMT model, I am trying to use BERT here, But I don't have any idea how to give it to my model. before that, I was using one-hot encoding and I got issues there and I want to use BERT.

Also, I have to note that, I am new in TensorFlow and deep learning. So please share your opinion with me about the use of BERT in NMT.

my goal is only to use BERT in my model for translation purpose

my model definition:

def build_model(in_vocab, out_vocab, in_timesteps, out_timesteps, units):
    model = Sequential()
    model.add(Embedding(in_vocab, units, input_length=in_timesteps, mask_zero=True))
    model.add(LSTM(units, return_sequences=True))
    model.add(Dense(out_vocab, activation='softmax'))
    return model

1 Answer 1


I find this way of using BERT in my translation system and it allows me to load and use more data to train my model. I got a memory error when I want to use more data like 100k for my task. and I came up that my tokenizer is a kind of problem here because it takes a lot of memory to make my tokenizer for such a huge volume of data so pre-trained models like BERT are the solution to this problem to feed more data like 200k or more to your model without worrying about memory error too much.

Also, in my task, I was worry about words that do not exist in my training phase but exist in a test phase so bert solved this problem for me too, because it was trained on a large corpus. let's dive in and find out how I used BERT to fix my problem here.

Here I am going to make English to English translation system.

  1. Loading pre-trained BERT for English ( if your source and the target language differs from each other, you have to load them separately you can look at tfhub.dev for them )
max_seq_length =  50 # i need to test the bert so I will keep this small for now
input_word_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32,name="input_word_ids")
input_mask = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32,name="input_mask")
segment_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32,name="segment_ids")
#this is the path to pre-trained bert model
bert_layer = hub.KerasLayer("https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/1",trainable=True)
  1. then I call my tokenizer
FullTokenizer = bert.bert_tokenization.FullTokenizer
vocab_file = bert_layer.resolved_object.vocab_file.asset_path.numpy()
do_lower_case = bert_layer.resolved_object.do_lower_case.numpy()
tokenizer = FullTokenizer(vocab_file, do_lower_case)

here is an examples to use the tokenizer:


the output is: ([2003], ['is'], ['##~'])

  1. then I used my train data to get my sequence
s = "This is a nice sentence."
stokens = tokenizer.tokenize(s)
stokens = ["[CLS]"] + stokens + ["[SEP]"]
input_ids = get_ids(stokens, tokenizer, max_seq_length)

the output for me was:

input Ids: [101, 2023, 2003, 1037, 3835, 6251, 1012, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
  1. this method is also needed for getting token ids and you should add it to your codes
def get_ids(tokens, tokenizer, max_seq_length):
    """Token ids from Tokenizer vocab"""
    token_ids = tokenizer.convert_tokens_to_ids(tokens)
    input_ids = token_ids + [0] * (max_seq_length-len(token_ids))
    return input_ids

  1. for your model vocab size you can use these values,

which in my case is: 30522

and after these steps, you can get your sequence for input and outputs and then feed them to your model.

I hope it helps. and please share your opinion if I am wrong in this case.


  • 1
    $\begingroup$ You might want to have a look at the huggingface implementation - github.com/huggingface/transformers $\endgroup$ Jan 8, 2020 at 6:11
  • 1
    $\begingroup$ thank you so much, I will check. $\endgroup$
    – Hamed
    Jan 8, 2020 at 21:13

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