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Terjemahan mesin saraf dengan perhatian

Lihat di TensorFlow.org Jalankan di Google Colab Lihat sumber di GitHub Unduh buku catatan

Notebook ini melatih model urutan ke urutan (seq2seq) untuk terjemahan Bahasa Spanyol ke Bahasa Inggris. Ini adalah contoh lanjutan yang mengasumsikan pengetahuan tentang urutan ke model urutan.

Setelah melatih model dalam buku catatan ini, Anda dapat memasukkan kalimat bahasa Spanyol, seperti "¿todavia estan en casa?" , dan kembalikan terjemahan bahasa Inggris: "apakah Anda masih di rumah?"

Kualitas terjemahan layak untuk contoh mainan, tetapi plot perhatian yang dihasilkan mungkin lebih menarik. Ini menunjukkan bagian kalimat input mana yang menarik perhatian model saat menerjemahkan:

plot perhatian spanyol-inggris

 import tensorflow as tf

import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from sklearn.model_selection import train_test_split

import unicodedata
import re
import numpy as np
import os
import io
import time
 

Unduh dan siapkan dataset

Kami akan menggunakan dataset bahasa yang disediakan oleh http://www.manythings.org/anki/ Dataset ini berisi pasangan terjemahan bahasa dalam format:

 May I borrow this book? ¿Puedo tomar prestado este libro?
 

Ada berbagai bahasa yang tersedia, tetapi kami akan menggunakan dataset bahasa Inggris-Spanyol. Untuk kenyamanan, kami telah meng-host salinan dataset ini di Google Cloud, tetapi Anda juga dapat mengunduh salinan Anda sendiri. Setelah mengunduh dataset, berikut adalah langkah-langkah yang akan kami ambil untuk menyiapkan data:

  1. Tambahkan token awal dan akhir untuk setiap kalimat.
  2. Bersihkan kalimat dengan menghapus karakter khusus.
  3. Buat indeks kata dan membalikkan indeks kata (kamus kamus dari kata → id dan id → kata).
  4. Pad setiap kalimat dengan panjang maksimal.
 # Download the file
path_to_zip = tf.keras.utils.get_file(
    'spa-eng.zip', origin='http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip',
    extract=True)

path_to_file = os.path.dirname(path_to_zip)+"/spa-eng/spa.txt"
 
Downloading data from http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip
2646016/2638744 [==============================] - 0s 0us/step

 # Converts the unicode file to ascii
def unicode_to_ascii(s):
  return ''.join(c for c in unicodedata.normalize('NFD', s)
      if unicodedata.category(c) != 'Mn')


def preprocess_sentence(w):
  w = unicode_to_ascii(w.lower().strip())

  # creating a space between a word and the punctuation following it
  # eg: "he is a boy." => "he is a boy ."
  # Reference:- https://stackoverflow.com/questions/3645931/python-padding-punctuation-with-white-spaces-keeping-punctuation
  w = re.sub(r"([?.!,¿])", r" \1 ", w)
  w = re.sub(r'[" "]+', " ", w)

  # replacing everything with space except (a-z, A-Z, ".", "?", "!", ",")
  w = re.sub(r"[^a-zA-Z?.!,¿]+", " ", w)

  w = w.strip()

  # adding a start and an end token to the sentence
  # so that the model know when to start and stop predicting.
  w = '<start> ' + w + ' <end>'
  return w
 
 en_sentence = u"May I borrow this book?"
sp_sentence = u"¿Puedo tomar prestado este libro?"
print(preprocess_sentence(en_sentence))
print(preprocess_sentence(sp_sentence).encode('utf-8'))
 
<start> may i borrow this book ? <end>
b'<start> \xc2\xbf puedo tomar prestado este libro ? <end>'

 # 1. Remove the accents
# 2. Clean the sentences
# 3. Return word pairs in the format: [ENGLISH, SPANISH]
def create_dataset(path, num_examples):
  lines = io.open(path, encoding='UTF-8').read().strip().split('\n')

  word_pairs = [[preprocess_sentence(w) for w in l.split('\t')]  for l in lines[:num_examples]]

  return zip(*word_pairs)
 
 en, sp = create_dataset(path_to_file, None)
print(en[-1])
print(sp[-1])
 
<start> if you want to sound like a native speaker , you must be willing to practice saying the same sentence over and over in the same way that banjo players practice the same phrase over and over until they can play it correctly and at the desired tempo . <end>
<start> si quieres sonar como un hablante nativo , debes estar dispuesto a practicar diciendo la misma frase una y otra vez de la misma manera en que un musico de banjo practica el mismo fraseo una y otra vez hasta que lo puedan tocar correctamente y en el tiempo esperado . <end>

 def tokenize(lang):
  lang_tokenizer = tf.keras.preprocessing.text.Tokenizer(
      filters='')
  lang_tokenizer.fit_on_texts(lang)

  tensor = lang_tokenizer.texts_to_sequences(lang)

  tensor = tf.keras.preprocessing.sequence.pad_sequences(tensor,
                                                         padding='post')

  return tensor, lang_tokenizer
 
 def load_dataset(path, num_examples=None):
  # creating cleaned input, output pairs
  targ_lang, inp_lang = create_dataset(path, num_examples)

  input_tensor, inp_lang_tokenizer = tokenize(inp_lang)
  target_tensor, targ_lang_tokenizer = tokenize(targ_lang)

  return input_tensor, target_tensor, inp_lang_tokenizer, targ_lang_tokenizer
 

Batasi ukuran dataset untuk bereksperimen lebih cepat (opsional)

Pelatihan tentang dataset lengkap> 100.000 kalimat akan memakan waktu lama. Untuk melatih lebih cepat, kami dapat membatasi ukuran dataset hingga 30.000 kalimat (tentu saja, kualitas terjemahan menurun dengan lebih sedikit data):

 # Try experimenting with the size of that dataset
num_examples = 30000
input_tensor, target_tensor, inp_lang, targ_lang = load_dataset(path_to_file, num_examples)

# Calculate max_length of the target tensors
max_length_targ, max_length_inp = target_tensor.shape[1], input_tensor.shape[1]
 
 # Creating training and validation sets using an 80-20 split
input_tensor_train, input_tensor_val, target_tensor_train, target_tensor_val = train_test_split(input_tensor, target_tensor, test_size=0.2)

# Show length
print(len(input_tensor_train), len(target_tensor_train), len(input_tensor_val), len(target_tensor_val))
 
24000 24000 6000 6000

 def convert(lang, tensor):
  for t in tensor:
    if t!=0:
      print ("%d ----> %s" % (t, lang.index_word[t]))
 
 print ("Input Language; index to word mapping")
convert(inp_lang, input_tensor_train[0])
print ()
print ("Target Language; index to word mapping")
convert(targ_lang, target_tensor_train[0])
 
Input Language; index to word mapping
1 ----> <start>
4 ----> tom
42 ----> tiene
2344 ----> tos
3 ----> .
2 ----> <end>

Target Language; index to word mapping
1 ----> <start>
5 ----> tom
51 ----> has
9 ----> a
1554 ----> cough
3 ----> .
2 ----> <end>

Buat dataset tf.data

 BUFFER_SIZE = len(input_tensor_train)
BATCH_SIZE = 64
steps_per_epoch = len(input_tensor_train)//BATCH_SIZE
embedding_dim = 256
units = 1024
vocab_inp_size = len(inp_lang.word_index)+1
vocab_tar_size = len(targ_lang.word_index)+1

dataset = tf.data.Dataset.from_tensor_slices((input_tensor_train, target_tensor_train)).shuffle(BUFFER_SIZE)
dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)
 
 example_input_batch, example_target_batch = next(iter(dataset))
example_input_batch.shape, example_target_batch.shape
 
(TensorShape([64, 16]), TensorShape([64, 11]))

Tulis model encoder dan decoder

Menerapkan model encoder-decoder dengan perhatian yang dapat Anda baca di tutorial Terjemahan Mesin TensorFlow Neural (seq2seq) . Contoh ini menggunakan set API yang lebih baru. Notebook ini mengimplementasikan persamaan perhatian dari tutorial seq2seq. Diagram berikut menunjukkan bahwa setiap kata input diberi bobot oleh mekanisme perhatian yang kemudian digunakan oleh decoder untuk memprediksi kata berikutnya dalam kalimat. Gambar dan formula di bawah ini adalah contoh mekanisme perhatian dari kertas Luong .

mekanisme perhatian

Input dimasukkan melalui model encoder yang memberi kami output encoder bentuk (batch_size, max_length, hidden_size) dan bentuk bentuk tersembunyi encoder (batch_size, hidden_size) .

Berikut adalah persamaan yang diterapkan:

persamaan perhatian 0persamaan perhatian 1

Tutorial ini menggunakan perhatian Bahdanau untuk pembuat enkode. Mari kita putuskan notasi sebelum menulis formulir yang disederhanakan:

  • FC = Lapisan sepenuhnya terhubung (padat)
  • EO = Output encoder
  • H = keadaan tersembunyi
  • X = input ke decoder

Dan pseudo-code:

  • score = FC(tanh(FC(EO) + FC(H)))
  • attention weights = softmax(score, axis = 1) . Softmax secara default diterapkan pada sumbu terakhir tetapi di sini kami ingin menerapkannya pada sumbu 1 , karena bentuk skornya adalah (batch_size, max_length, hidden_size) . Max_length adalah panjang input kami. Karena kami mencoba menetapkan bobot untuk setiap input, softmax harus diterapkan pada sumbu itu.
  • context vector = sum(attention weights * EO, axis = 1) . Alasan yang sama seperti di atas untuk memilih sumbu sebagai 1.
  • embedding output = Input ke decoder X dilewatkan melalui lapisan embedding.
  • merged vector = concat(embedding output, context vector)
  • Vektor gabungan ini kemudian diberikan kepada GRU

Bentuk semua vektor pada setiap langkah telah ditentukan dalam komentar dalam kode:

 class Encoder(tf.keras.Model):
  def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz):
    super(Encoder, self).__init__()
    self.batch_sz = batch_sz
    self.enc_units = enc_units
    self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
    self.gru = tf.keras.layers.GRU(self.enc_units,
                                   return_sequences=True,
                                   return_state=True,
                                   recurrent_initializer='glorot_uniform')

  def call(self, x, hidden):
    x = self.embedding(x)
    output, state = self.gru(x, initial_state = hidden)
    return output, state

  def initialize_hidden_state(self):
    return tf.zeros((self.batch_sz, self.enc_units))
 
 encoder = Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE)

# sample input
sample_hidden = encoder.initialize_hidden_state()
sample_output, sample_hidden = encoder(example_input_batch, sample_hidden)
print ('Encoder output shape: (batch size, sequence length, units) {}'.format(sample_output.shape))
print ('Encoder Hidden state shape: (batch size, units) {}'.format(sample_hidden.shape))
 
Encoder output shape: (batch size, sequence length, units) (64, 16, 1024)
Encoder Hidden state shape: (batch size, units) (64, 1024)

 class BahdanauAttention(tf.keras.layers.Layer):
  def __init__(self, units):
    super(BahdanauAttention, self).__init__()
    self.W1 = tf.keras.layers.Dense(units)
    self.W2 = tf.keras.layers.Dense(units)
    self.V = tf.keras.layers.Dense(1)

  def call(self, query, values):
    # query hidden state shape == (batch_size, hidden size)
    # query_with_time_axis shape == (batch_size, 1, hidden size)
    # values shape == (batch_size, max_len, hidden size)
    # we are doing this to broadcast addition along the time axis to calculate the score
    query_with_time_axis = tf.expand_dims(query, 1)

    # score shape == (batch_size, max_length, 1)
    # we get 1 at the last axis because we are applying score to self.V
    # the shape of the tensor before applying self.V is (batch_size, max_length, units)
    score = self.V(tf.nn.tanh(
        self.W1(query_with_time_axis) + self.W2(values)))

    # attention_weights shape == (batch_size, max_length, 1)
    attention_weights = tf.nn.softmax(score, axis=1)

    # context_vector shape after sum == (batch_size, hidden_size)
    context_vector = attention_weights * values
    context_vector = tf.reduce_sum(context_vector, axis=1)

    return context_vector, attention_weights
 
 attention_layer = BahdanauAttention(10)
attention_result, attention_weights = attention_layer(sample_hidden, sample_output)

print("Attention result shape: (batch size, units) {}".format(attention_result.shape))
print("Attention weights shape: (batch_size, sequence_length, 1) {}".format(attention_weights.shape))
 
Attention result shape: (batch size, units) (64, 1024)
Attention weights shape: (batch_size, sequence_length, 1) (64, 16, 1)

 class Decoder(tf.keras.Model):
  def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz):
    super(Decoder, self).__init__()
    self.batch_sz = batch_sz
    self.dec_units = dec_units
    self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
    self.gru = tf.keras.layers.GRU(self.dec_units,
                                   return_sequences=True,
                                   return_state=True,
                                   recurrent_initializer='glorot_uniform')
    self.fc = tf.keras.layers.Dense(vocab_size)

    # used for attention
    self.attention = BahdanauAttention(self.dec_units)

  def call(self, x, hidden, enc_output):
    # enc_output shape == (batch_size, max_length, hidden_size)
    context_vector, attention_weights = self.attention(hidden, enc_output)

    # x shape after passing through embedding == (batch_size, 1, embedding_dim)
    x = self.embedding(x)

    # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size)
    x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)

    # passing the concatenated vector to the GRU
    output, state = self.gru(x)

    # output shape == (batch_size * 1, hidden_size)
    output = tf.reshape(output, (-1, output.shape[2]))

    # output shape == (batch_size, vocab)
    x = self.fc(output)

    return x, state, attention_weights
 
 decoder = Decoder(vocab_tar_size, embedding_dim, units, BATCH_SIZE)

sample_decoder_output, _, _ = decoder(tf.random.uniform((BATCH_SIZE, 1)),
                                      sample_hidden, sample_output)

print ('Decoder output shape: (batch_size, vocab size) {}'.format(sample_decoder_output.shape))
 
Decoder output shape: (batch_size, vocab size) (64, 4935)

Tentukan pengoptimal dan fungsi kerugian

 optimizer = tf.keras.optimizers.Adam()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(
    from_logits=True, reduction='none')

def loss_function(real, pred):
  mask = tf.math.logical_not(tf.math.equal(real, 0))
  loss_ = loss_object(real, pred)

  mask = tf.cast(mask, dtype=loss_.dtype)
  loss_ *= mask

  return tf.reduce_mean(loss_)
 

Pos pemeriksaan (Penghematan berbasis objek)

 checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(optimizer=optimizer,
                                 encoder=encoder,
                                 decoder=decoder)
 

Latihan

  1. Lewati input melalui encoder yang mengembalikan output encoder dan status tersembunyi encoder .
  2. Output encoder, keadaan tersembunyi encoder dan input decoder (yang merupakan token awal ) diteruskan ke decoder.
  3. Decoder mengembalikan prediksi dan status tersembunyi decoder .
  4. Status tersembunyi decoder kemudian dilewatkan kembali ke dalam model dan prediksi digunakan untuk menghitung kerugian.
  5. Gunakan memaksa guru untuk memutuskan input selanjutnya ke decoder.
  6. Guru memaksa adalah teknik di mana kata target dilewatkan sebagai input selanjutnya ke decoder.
  7. Langkah terakhir adalah menghitung gradien dan menerapkannya ke optimizer dan backpropagate.
 @tf.function
def train_step(inp, targ, enc_hidden):
  loss = 0

  with tf.GradientTape() as tape:
    enc_output, enc_hidden = encoder(inp, enc_hidden)

    dec_hidden = enc_hidden

    dec_input = tf.expand_dims([targ_lang.word_index['<start>']] * BATCH_SIZE, 1)

    # Teacher forcing - feeding the target as the next input
    for t in range(1, targ.shape[1]):
      # passing enc_output to the decoder
      predictions, dec_hidden, _ = decoder(dec_input, dec_hidden, enc_output)

      loss += loss_function(targ[:, t], predictions)

      # using teacher forcing
      dec_input = tf.expand_dims(targ[:, t], 1)

  batch_loss = (loss / int(targ.shape[1]))

  variables = encoder.trainable_variables + decoder.trainable_variables

  gradients = tape.gradient(loss, variables)

  optimizer.apply_gradients(zip(gradients, variables))

  return batch_loss
 
 EPOCHS = 10

for epoch in range(EPOCHS):
  start = time.time()

  enc_hidden = encoder.initialize_hidden_state()
  total_loss = 0

  for (batch, (inp, targ)) in enumerate(dataset.take(steps_per_epoch)):
    batch_loss = train_step(inp, targ, enc_hidden)
    total_loss += batch_loss

    if batch % 100 == 0:
      print('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1,
                                                   batch,
                                                   batch_loss.numpy()))
  # saving (checkpoint) the model every 2 epochs
  if (epoch + 1) % 2 == 0:
    checkpoint.save(file_prefix = checkpoint_prefix)

  print('Epoch {} Loss {:.4f}'.format(epoch + 1,
                                      total_loss / steps_per_epoch))
  print('Time taken for 1 epoch {} sec\n'.format(time.time() - start))
 
Epoch 1 Batch 0 Loss 4.4937
Epoch 1 Batch 100 Loss 2.3472
Epoch 1 Batch 200 Loss 1.9153
Epoch 1 Batch 300 Loss 1.8042
Epoch 1 Loss 2.0265
Time taken for 1 epoch 27.345187664031982 sec

Epoch 2 Batch 0 Loss 1.5260
Epoch 2 Batch 100 Loss 1.5228
Epoch 2 Batch 200 Loss 1.3840
Epoch 2 Batch 300 Loss 1.3131
Epoch 2 Loss 1.3900
Time taken for 1 epoch 15.777411222457886 sec

Epoch 3 Batch 0 Loss 1.0458
Epoch 3 Batch 100 Loss 0.9216
Epoch 3 Batch 200 Loss 0.9254
Epoch 3 Batch 300 Loss 0.9041
Epoch 3 Loss 0.9699
Time taken for 1 epoch 15.391497373580933 sec

Epoch 4 Batch 0 Loss 0.7582
Epoch 4 Batch 100 Loss 0.7201
Epoch 4 Batch 200 Loss 0.6765
Epoch 4 Batch 300 Loss 0.6696
Epoch 4 Loss 0.6555
Time taken for 1 epoch 15.782341480255127 sec

Epoch 5 Batch 0 Loss 0.3534
Epoch 5 Batch 100 Loss 0.4191
Epoch 5 Batch 200 Loss 0.5322
Epoch 5 Batch 300 Loss 0.4767
Epoch 5 Loss 0.4494
Time taken for 1 epoch 15.508086204528809 sec

Epoch 6 Batch 0 Loss 0.2508
Epoch 6 Batch 100 Loss 0.3366
Epoch 6 Batch 200 Loss 0.2935
Epoch 6 Batch 300 Loss 0.3432
Epoch 6 Loss 0.3137
Time taken for 1 epoch 15.811218738555908 sec

Epoch 7 Batch 0 Loss 0.1759
Epoch 7 Batch 100 Loss 0.1997
Epoch 7 Batch 200 Loss 0.2879
Epoch 7 Batch 300 Loss 0.2643
Epoch 7 Loss 0.2257
Time taken for 1 epoch 15.454826831817627 sec

Epoch 8 Batch 0 Loss 0.1318
Epoch 8 Batch 100 Loss 0.1151
Epoch 8 Batch 200 Loss 0.2130
Epoch 8 Batch 300 Loss 0.1852
Epoch 8 Loss 0.1712
Time taken for 1 epoch 15.786991596221924 sec

Epoch 9 Batch 0 Loss 0.0876
Epoch 9 Batch 100 Loss 0.1227
Epoch 9 Batch 200 Loss 0.1361
Epoch 9 Batch 300 Loss 0.1682
Epoch 9 Loss 0.1328
Time taken for 1 epoch 15.443743467330933 sec

Epoch 10 Batch 0 Loss 0.1048
Epoch 10 Batch 100 Loss 0.0736
Epoch 10 Batch 200 Loss 0.1056
Epoch 10 Batch 300 Loss 0.1204
Epoch 10 Loss 0.1074
Time taken for 1 epoch 15.615742683410645 sec


Menterjemahkan

  • Fungsi evaluasi mirip dengan loop pelatihan, kecuali kami tidak menggunakan memaksa guru di sini. Input ke decoder pada setiap langkah waktu adalah prediksi sebelumnya bersama dengan status tersembunyi dan output encoder.
  • Berhenti memprediksi ketika model memprediksi token akhir .
  • Dan simpan bobot perhatian untuk setiap langkah waktu .
 def evaluate(sentence):
  attention_plot = np.zeros((max_length_targ, max_length_inp))

  sentence = preprocess_sentence(sentence)

  inputs = [inp_lang.word_index[i] for i in sentence.split(' ')]
  inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs],
                                                         maxlen=max_length_inp,
                                                         padding='post')
  inputs = tf.convert_to_tensor(inputs)

  result = ''

  hidden = [tf.zeros((1, units))]
  enc_out, enc_hidden = encoder(inputs, hidden)

  dec_hidden = enc_hidden
  dec_input = tf.expand_dims([targ_lang.word_index['<start>']], 0)

  for t in range(max_length_targ):
    predictions, dec_hidden, attention_weights = decoder(dec_input,
                                                         dec_hidden,
                                                         enc_out)

    # storing the attention weights to plot later on
    attention_weights = tf.reshape(attention_weights, (-1, ))
    attention_plot[t] = attention_weights.numpy()

    predicted_id = tf.argmax(predictions[0]).numpy()

    result += targ_lang.index_word[predicted_id] + ' '

    if targ_lang.index_word[predicted_id] == '<end>':
      return result, sentence, attention_plot

    # the predicted ID is fed back into the model
    dec_input = tf.expand_dims([predicted_id], 0)

  return result, sentence, attention_plot
 
 # function for plotting the attention weights
def plot_attention(attention, sentence, predicted_sentence):
  fig = plt.figure(figsize=(10,10))
  ax = fig.add_subplot(1, 1, 1)
  ax.matshow(attention, cmap='viridis')

  fontdict = {'fontsize': 14}

  ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90)
  ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)

  ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
  ax.yaxis.set_major_locator(ticker.MultipleLocator(1))

  plt.show()
 
 def translate(sentence):
  result, sentence, attention_plot = evaluate(sentence)

  print('Input: %s' % (sentence))
  print('Predicted translation: {}'.format(result))

  attention_plot = attention_plot[:len(result.split(' ')), :len(sentence.split(' '))]
  plot_attention(attention_plot, sentence.split(' '), result.split(' '))
 

Kembalikan pos pemeriksaan dan tes terbaru

 # restoring the latest checkpoint in checkpoint_dir
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
 
<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7ff366a4bcc0>
 translate(u'hace mucho frio aqui.')
 
Input: <start> hace mucho frio aqui . <end>
Predicted translation: it s very cold here . <end> 

png

 translate(u'esta es mi vida.')
 
Input: <start> esta es mi vida . <end>
Predicted translation: this is my life . <end> 

png

 translate(u'¿todavia estan en casa?')
 
Input: <start> ¿ todavia estan en casa ? <end>
Predicted translation: are you still at home ? <end> 

png

 # wrong translation
translate(u'trata de averiguarlo.')
 
Input: <start> trata de averiguarlo . <end>
Predicted translation: try to figure it out . <end> 

png

Langkah selanjutnya

  • Unduh dataset berbeda untuk bereksperimen dengan terjemahan, misalnya, Bahasa Inggris ke Bahasa Jerman, atau Bahasa Inggris ke Bahasa Prancis.
  • Eksperimen dengan pelatihan pada dataset yang lebih besar, atau menggunakan lebih banyak zaman