» Code examples / Natural Language Processing / English-to-Spanish translation with KerasNLP

English-to-Spanish translation with KerasNLP

Author: Abheesht Sharma
Date created: 2022/05/26
Last modified: 2022/05/26
Description: Use KerasNLP to train a sequence-to-sequence Transformer model on the machine translation task.

View in Colab GitHub source


Introduction

KerasNLP provides building blocks for NLP (model layers, tokenizers, metrics, etc.) and makes it convenient to construct NLP pipelines.

In this example, we'll use KerasNLP layers to build an encoder-decoder Transformer model, and train it on the English-to-Spanish machine translation task.

This example is based on the English-to-Spanish NMT example by fchollet. The original example is more low-level and implements layers from scratch, whereas this example uses KerasNLP to show some more advanced approaches, such as subword tokenization and using metrics to compute the quality of generated translations.

You'll learn how to:

Don't worry if you aren't familiar with KerasNLP. This tutorial will start with the basics. Let's dive right in!


Setup

Before we start implementing the pipeline, let's import all the libraries we need.

!pip install -q rouge-score
import keras_nlp
import numpy as np
import pathlib
import random
import tensorflow as tf

from tensorflow import keras
from tensorflow_text.tools.wordpiece_vocab import bert_vocab_from_dataset as bert_vocab

Let's also define our parameters/hyperparameters.

BATCH_SIZE = 64
EPOCHS = 1  # This should be at least 10 for convergence
MAX_SEQUENCE_LENGTH = 40
ENG_VOCAB_SIZE = 15000
SPA_VOCAB_SIZE = 15000

EMBED_DIM = 256
INTERMEDIATE_DIM = 2048
NUM_HEADS = 8

Downloading the data

We'll be working with an English-to-Spanish translation dataset provided by Anki. Let's download it:

text_file = keras.utils.get_file(
    fname="spa-eng.zip",
    origin="http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip",
    extract=True,
)
text_file = pathlib.Path(text_file).parent / "spa-eng" / "spa.txt"

Parsing the data

Each line contains an English sentence and its corresponding Spanish sentence. The English sentence is the source sequence and Spanish one is the target sequence. Before adding the text to a list, we convert it to lowercase.

with open(text_file) as f:
    lines = f.read().split("\n")[:-1]
text_pairs = []
for line in lines:
    eng, spa = line.split("\t")
    eng = eng.lower()
    spa = spa.lower()
    text_pairs.append((eng, spa))

Here's what our sentence pairs look like:

for _ in range(5):
    print(random.choice(text_pairs))
('america is a country of immigrants.', 'estados unidos es un país de inmigrantes.')
('do you hear the birds singing?', '¿oyes cantar a los pájaros?')
('if you want to get out of here alive, follow me.', 'sígueme si quieres salir de aquí con vida.')
('the rain lasted the whole night.', 'la lluvia duró toda la noche.')
('tom wanted it this way.', 'tom lo quería así.')

Now, let's split the sentence pairs into a training set, a validation set, and a test set.

random.shuffle(text_pairs)
num_val_samples = int(0.15 * len(text_pairs))
num_train_samples = len(text_pairs) - 2 * num_val_samples
train_pairs = text_pairs[:num_train_samples]
val_pairs = text_pairs[num_train_samples : num_train_samples + num_val_samples]
test_pairs = text_pairs[num_train_samples + num_val_samples :]

print(f"{len(text_pairs)} total pairs")
print(f"{len(train_pairs)} training pairs")
print(f"{len(val_pairs)} validation pairs")
print(f"{len(test_pairs)} test pairs")
118964 total pairs
83276 training pairs
17844 validation pairs
17844 test pairs

Tokenizing the data

We'll define two tokenizers - one for the source language (English), and the other for the target language (Spanish). We'll be using keras_nlp.tokenizers.WordPieceTokenizer to tokenize the text. keras_nlp.tokenizers.WordPieceTokenizer takes a WordPiece vocabulary and has functions for tokenizing the text, and detokenizing sequences of tokens.

Before we define the two tokenizers, we first need to train them on the dataset we have. The WordPiece tokenization algorithm is a subword tokenization algorithm; training it on a corpus gives us a vocabulary of subwords. A subword tokenizer is a compromise between word tokenizers (word tokenizers need very large vocabularies for good coverage of input words), and character tokenizers (characters don't really encode meaning like words do). Luckily, TensorFlow Text makes it very simple to train WordPiece on a corpus as described in this guide.

def train_word_piece(text_samples, vocab_size, reserved_tokens):
    bert_vocab_args = dict(
        # The target vocabulary size
        vocab_size=vocab_size,
        # Reserved tokens that must be included in the vocabulary
        reserved_tokens=reserved_tokens,
        # Arguments for `text.BertTokenizer`
        bert_tokenizer_params={"lower_case": True},
    )

    word_piece_ds = tf.data.Dataset.from_tensor_slices(text_samples)
    vocab = bert_vocab.bert_vocab_from_dataset(
        word_piece_ds.batch(1000).prefetch(2), **bert_vocab_args
    )
    return vocab

Every vocabulary has a few special, reserved tokens. We have four such tokens:

  • "[PAD]" - Padding token. Padding tokens are appended to the input sequence length when the input sequence length is shorter than the maximum sequence length.
  • "[UNK]" - Unknown token.
  • "[START]" - Token that marks the start of the input sequence.
  • "[END]" - Token that marks the end of the input sequence.
reserved_tokens = ["[PAD]", "[UNK]", "[START]", "[END]"]

eng_samples = [text_pair[0] for text_pair in train_pairs]
eng_vocab = train_word_piece(eng_samples, ENG_VOCAB_SIZE, reserved_tokens)

spa_samples = [text_pair[1] for text_pair in train_pairs]
spa_vocab = train_word_piece(spa_samples, SPA_VOCAB_SIZE, reserved_tokens)

Let's see some tokens!

print("English Tokens: ", eng_vocab[100:110])
print("Spanish Tokens: ", spa_vocab[100:110])
English Tokens:  ['as', 'll', 'did', 'very', 'had', 'all', 'here', 'up', 'about', 'didn']
Spanish Tokens:  ['estaba', 'tengo', 'fue', 'quiero', 'aqui', 'casa', 'cuando', 'hacer', '##n', 'puedo']

Now, let's define the tokenizers. We will configure the tokenizers with the the vocabularies trained above.

eng_tokenizer = keras_nlp.tokenizers.WordPieceTokenizer(
    vocabulary=eng_vocab, lowercase=False
)
spa_tokenizer = keras_nlp.tokenizers.WordPieceTokenizer(
    vocabulary=spa_vocab, lowercase=False
)

Let's try and tokenize a sample from our dataset! To verify whether the text has been tokenized correctly, we can also detokenize the list of tokens back to the original text.

eng_input_ex = text_pairs[0][0]
eng_tokens_ex = eng_tokenizer.tokenize(eng_input_ex)
print("English sentence: ", eng_input_ex)
print("Tokens: ", eng_tokens_ex)
print("Recovered text after detokenizing: ", eng_tokenizer.detokenize(eng_tokens_ex))

print()

spa_input_ex = text_pairs[0][1]
spa_tokens_ex = spa_tokenizer.tokenize(spa_input_ex)
print("Spanish sentence: ", spa_input_ex)
print("Tokens: ", spa_tokens_ex)
print("Recovered text after detokenizing: ", spa_tokenizer.detokenize(spa_tokens_ex))
English sentence:  i didn't think it was so bad.
Tokens:  tf.Tensor([ 33 109   8  44 110  60  64 135 297  11], shape=(10,), dtype=int32)
Recovered text after detokenizing:  tf.Tensor(b"i didn ' t think it was so bad .", shape=(), dtype=string)
Spanish sentence:  no pensé que era tan malo.
Tokens:  tf.Tensor([ 65 237  62 124 119 629  14], shape=(7,), dtype=int32)
Recovered text after detokenizing:  tf.Tensor(b'no pense que era tan malo .', shape=(), dtype=string)

Format datasets

Next, we'll format our datasets.

At each training step, the model will seek to predict target words N+1 (and beyond) using the source sentence and the target words 0 to N.

As such, the training dataset will yield a tuple (inputs, targets), where:

  • inputs is a dictionary with the keys encoder_inputs and decoder_inputs. encoder_inputs is the tokenized source sentence and decoder_inputs is the target sentence "so far", that is to say, the words 0 to N used to predict word N+1 (and beyond) in the target sentence.
  • target is the target sentence offset by one step: it provides the next words in the target sentence -- what the model will try to predict.

We will add special tokens, "[START]" and "[END]", to the input Spanish sentence after tokenizing the text. We will also pad the input to a fixed length. This can be easily done using keras_nlp.layers.StartEndPacker.

def preprocess_batch(eng, spa):
    batch_size = tf.shape(spa)[0]

    eng = eng_tokenizer(eng)
    spa = spa_tokenizer(spa)

    # Pad `eng` to `MAX_SEQUENCE_LENGTH`.
    eng_start_end_packer = keras_nlp.layers.StartEndPacker(
        sequence_length=MAX_SEQUENCE_LENGTH,
        pad_value=eng_tokenizer.token_to_id("[PAD]"),
    )
    eng = eng_start_end_packer(eng)

    # Add special tokens (`"[START]"` and `"[END]"`) to `spa` and pad it as well.
    spa_start_end_packer = keras_nlp.layers.StartEndPacker(
        sequence_length=MAX_SEQUENCE_LENGTH + 1,
        start_value=spa_tokenizer.token_to_id("[START]"),
        end_value=spa_tokenizer.token_to_id("[END]"),
        pad_value=spa_tokenizer.token_to_id("[PAD]"),
    )
    spa = spa_start_end_packer(spa)

    return (
        {
            "encoder_inputs": eng,
            "decoder_inputs": spa[:, :-1],
        },
        spa[:, 1:],
    )


def make_dataset(pairs):
    eng_texts, spa_texts = zip(*pairs)
    eng_texts = list(eng_texts)
    spa_texts = list(spa_texts)
    dataset = tf.data.Dataset.from_tensor_slices((eng_texts, spa_texts))
    dataset = dataset.batch(BATCH_SIZE)
    dataset = dataset.map(preprocess_batch, num_parallel_calls=tf.data.AUTOTUNE)
    return dataset.shuffle(2048).prefetch(16).cache()


train_ds = make_dataset(train_pairs)
val_ds = make_dataset(val_pairs)

Let's take a quick look at the sequence shapes (we have batches of 64 pairs, and all sequences are 40 steps long):

for inputs, targets in train_ds.take(1):
    print(f'inputs["encoder_inputs"].shape: {inputs["encoder_inputs"].shape}')
    print(f'inputs["decoder_inputs"].shape: {inputs["decoder_inputs"].shape}')
    print(f"targets.shape: {targets.shape}")
inputs["encoder_inputs"].shape: (64, 40)
inputs["decoder_inputs"].shape: (64, 40)
targets.shape: (64, 40)

Building the model

Now, let's move on to the exciting part - defining our model! We first need an embedding layer, i.e., a vector for every token in our input sequence. This embedding layer can be initialised randomly. We also need a positional embedding layer which encodes the word order in the sequence. The convention is to add these two embeddings. KerasNLP has a keras_nlp.layers.TokenAndPositionEmbedding layer which does all of the above steps for us.

Our sequence-to-sequence Transformer consists of a keras_nlp.layers.TransformerEncoder layer and a keras_nlp.layers.TransformerDecoder layer chained together.

The source sequence will be passed to keras_nlp.layers.TransformerEncoder, which will produce a new representation of it. This new representation will then be passed to the keras_nlp.layers.TransformerDecoder, together with the target sequence so far (target words 0 to N). The keras_nlp.layers.TransformerDecoder will then seek to predict the next words in the target sequence (N+1 and beyond).

A key detail that makes this possible is causal masking. The keras_nlp.layers.TransformerDecoder sees the entire sequence at once, and thus we must make sure that it only uses information from target tokens 0 to N when predicting token N+1 (otherwise, it could use information from the future, which would result in a model that cannot be used at inference time). Causal masking is enabled by default in keras_nlp.layers.TransformerDecoder.

We also need to mask the padding tokens ("[PAD]"). For this, we can set the mask_zero argument of the keras_nlp.layers.TokenAndPositionEmbedding layer to True. This will then be propagated to all subsequent layers.

# Encoder
encoder_inputs = keras.Input(shape=(None,), dtype="int64", name="encoder_inputs")

x = keras_nlp.layers.TokenAndPositionEmbedding(
    vocabulary_size=ENG_VOCAB_SIZE,
    sequence_length=MAX_SEQUENCE_LENGTH,
    embedding_dim=EMBED_DIM,
    mask_zero=True,
)(encoder_inputs)

encoder_outputs = keras_nlp.layers.TransformerEncoder(
    intermediate_dim=INTERMEDIATE_DIM, num_heads=NUM_HEADS
)(inputs=x)
encoder = keras.Model(encoder_inputs, encoder_outputs)


# Decoder
decoder_inputs = keras.Input(shape=(None,), dtype="int64", name="decoder_inputs")
encoded_seq_inputs = keras.Input(shape=(None, EMBED_DIM), name="decoder_state_inputs")

x = keras_nlp.layers.TokenAndPositionEmbedding(
    vocabulary_size=SPA_VOCAB_SIZE,
    sequence_length=MAX_SEQUENCE_LENGTH,
    embedding_dim=EMBED_DIM,
    mask_zero=True,
)(decoder_inputs)

x = keras_nlp.layers.TransformerDecoder(
    intermediate_dim=INTERMEDIATE_DIM, num_heads=NUM_HEADS
)(decoder_sequence=x, encoder_sequence=encoded_seq_inputs)
x = keras.layers.Dropout(0.5)(x)
decoder_outputs = keras.layers.Dense(SPA_VOCAB_SIZE, activation="softmax")(x)
decoder = keras.Model(
    [
        decoder_inputs,
        encoded_seq_inputs,
    ],
    decoder_outputs,
)
decoder_outputs = decoder([decoder_inputs, encoder_outputs])

transformer = keras.Model(
    [encoder_inputs, decoder_inputs],
    decoder_outputs,
    name="transformer",
)

Training our model

We'll use accuracy as a quick way to monitor training progress on the validation data. Note that machine translation typically uses BLEU scores as well as other metrics, rather than accuracy. However, in order to use metrics like ROUGE, BLEU, etc. we will have decode the probabilities and generate the text. Text generation is computationally expensive, and performing this during training is not recommended.

Here we only train for 1 epoch, but to get the model to actually converge you should train for at least 10 epochs.

transformer.summary()
transformer.compile(
    "rmsprop", loss="sparse_categorical_crossentropy", metrics=["accuracy"]
)
transformer.fit(train_ds, epochs=EPOCHS, validation_data=val_ds)
Model: "transformer"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 encoder_inputs (InputLayer)    [(None, None)]       0           []                               

 token_and_position_embedding (  (None, None, 256)   3850240     ['encoder_inputs[0][0]']         
 TokenAndPositionEmbedding)                                                                       

 decoder_inputs (InputLayer)    [(None, None)]       0           []                               

 transformer_encoder (Transform  (None, None, 256)   1315072     ['token_and_position_embedding[0]
 erEncoder)                                                      [0]']                            

 model_1 (Functional)           (None, None, 15000)  10203288    ['decoder_inputs[0][0]',         
                                                                  'transformer_encoder[0][0]']    

==================================================================================================
Total params: 15,368,600
Trainable params: 15,368,600
Non-trainable params: 0
__________________________________________________________________________________________________
1302/1302 [==============================] - 107s 78ms/step - loss: 1.0684 - accuracy: 0.3975 - val_loss: 0.8740 - val_accuracy: 0.4836

<keras.callbacks.History at 0x7fe8705e1150>

Decoding test sentences (qualitative analysis)

Finally, let's demonstrate how to translate brand new English sentences. We simply feed into the model the tokenized English sentence as well as the target token "[START]". The model outputs probabilities of the next token. We then we repeatedly generated the next token conditioned on the tokens generated so far, until we hit the token "[END]".

For decoding, we will use the keras_nlp.utils.greedy_search function from KerasNLP. Greedy Decoding is a text decoding method which outputs the most likely next token at each time step, i.e., the token with the highest probability.

def decode_sequences(input_sentences):
    batch_size = tf.shape(input_sentences)[0]

    # Tokenize the encoder input.
    encoder_input_tokens = eng_tokenizer(input_sentences).to_tensor(
        shape=(None, MAX_SEQUENCE_LENGTH)
    )

    # Define a function that outputs the next token's probability given the
    # input sequence.
    def token_probability_fn(decoder_input_tokens):
        return transformer([encoder_input_tokens, decoder_input_tokens])[:, -1, :]

    # Set the prompt to the "[START]" token.
    prompt = tf.fill((batch_size, 1), spa_tokenizer.token_to_id("[START]"))

    generated_tokens = keras_nlp.utils.greedy_search(
        token_probability_fn,
        prompt,
        max_length=40,
        end_token_id=spa_tokenizer.token_to_id("[END]"),
    )
    generated_sentences = spa_tokenizer.detokenize(generated_tokens)
    return generated_sentences


test_eng_texts = [pair[0] for pair in test_pairs]
for i in range(2):
    input_sentence = random.choice(test_eng_texts)
    translated = decode_sequences(tf.constant([input_sentence]))
    translated = translated.numpy()[0].decode("utf-8")
    translated = (
        translated.replace("[PAD]", "")
        .replace("[START]", "")
        .replace("[END]", "")
        .strip()
    )
    print(f"** Example {i} **")
    print(input_sentence)
    print(translated)
    print()
** Example 0 **
the workers asked for an increase in pay.
el compumpumpulin a un mes en un mes .
** Example 1 **
my brother insisted on going there alone.
mi padre se hizo tarde .

After 10 epochs, we get samples like these:

Example 0

have you seen this?

¿has visto esto?

Example 1

it's very hot here.

hace mucho calor aqui.

Example 2

my mother always says she's going to visit me soon.

mi madre es algo de decir que ella me va a visitar pronto.

Example 3

you can't say that.

no puedes decir eso.

Example 4

there are always some chores to be done around the house.

siempre hay algunos chance para hacer cerca de la casa.

Example 5

sometimes the boys would play a joke on the teacher.

a veces los ninos tocaria una broma con el profesor.

Example 6

move this table toward the corner.

muevile esta mesa hacia la car, se muelvale esta mesa.

Example 7

tom has never heard mary sing.

tom nunca ha oído cantar a mary.

Example 8

tom was in town monday night.

tom estaba en la noche el lunes por la noche.

Example 9

what's happened, has happened. it's history.

lo que paso, ha pasado. es historia.


Evaluating our model (quantitative analysis)

There are many metrics which are used for text generation tasks. Here, to evaluate translations generated by our model, let's compute the ROUGE-1 and ROUGE-2 scores. Essentially, ROUGE-N is a score based on the number of common n-grams between the reference text and the generated text. ROUGE-1 and ROUGE-2 use the number of common unigrams and bigrams, respectively.

We will calculate the score over 30 test samples (since decoding is an expensive process).

rouge_1 = keras_nlp.metrics.RougeN(order=1)
rouge_2 = keras_nlp.metrics.RougeN(order=2)

for test_pair in test_pairs[:30]:
    input_sentence = test_pair[0]
    reference_sentence = test_pair[1]

    translated_sentence = decode_sequences(tf.constant([input_sentence]))
    translated_sentence = translated_sentence.numpy()[0].decode("utf-8")
    translated_sentence = (
        translated_sentence.replace("[PAD]", "")
        .replace("[START]", "")
        .replace("[END]", "")
        .strip()
    )

    rouge_1(reference_sentence, translated_sentence)
    rouge_2(reference_sentence, translated_sentence)

print("ROUGE-1 Score: ", rouge_1.result())
print("ROUGE-2 Score: ", rouge_2.result())
ROUGE-1 Score:  {'precision': <tf.Tensor: shape=(), dtype=float32, numpy=0.24886957>, 'recall': <tf.Tensor: shape=(), dtype=float32, numpy=0.19965802>, 'f1_score': <tf.Tensor: shape=(), dtype=float32, numpy=0.21695943>}
ROUGE-2 Score:  {'precision': <tf.Tensor: shape=(), dtype=float32, numpy=0.07670634>, 'recall': <tf.Tensor: shape=(), dtype=float32, numpy=0.044060845>, 'f1_score': <tf.Tensor: shape=(), dtype=float32, numpy=0.05444546>}

After 10 epochs, the scores are as follows:

ROUGE-1 ROUGE-2
Precision 0.468 0.245
Recall 0.456 0.230
F1 Score 0.457 0.236