On the difficulty of reading numbers in different languages

This blog post illustrates how difficult it is for a simple seq2seq model to learn how to translate numbers from different languages (e.g. French, English, Chinese, Malay) to their digits (base 10) representation. It is based on the very good deep learning tutorials by Olivier Grisel and Charles Ollion. Note that this is a very simple seq2seq model, cf. fairseq or sockeye for more sophisticated ones.

The experiment: We illustrate the convergence of the model to perfect prediction on the test set as a function of the training set size. Faster increasing accuracy indicates easier learning task, i.e. the model requires less training examples. The training set consists in randomly chosen numbers between 1 and 999,999. The model is fed with the language representation in input and has to output its digit (base 10) representation.

TL;DR

• Chinese is the easiest to learn, then French (despite its seemingly many particular cases such as ‘vingt’ vs. ‘vingts’ and ‘cent’ vs. ‘cents’), closely followed by Malay. English is not that easy (maybe because of the ‘-‘s that have to be forgotten).

• By looking at French examples, we might think that the model acquire some basic reasoning on arithmetic. Consider:

• “quatre vingts”, literally meaning “four twenty”, stands for “80” (and not 420), i.e. it can be interpreted as the multiplication of four by twenty; Or even more complicated:
• “quatre vingt onze mille”, literally meaning “four twenty eleven thousand”, which stands for “91000” (and not 420111000), i.e. it has to be interpreted as (4 * 20 + 11) * 1000.

To check whether the model is able to acquire some basic arithmetic skills, we have added the task of translating from hexadecimal to base 10 digits. Considering its poor results, it is unlikely that the model learns any arithmetic at all for performing its translation task. However, this task is more difficult (implicit base 16, and exponentiation based on the digit position). More on that in later posts…

``````from collections import OrderedDict

import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Embedding, Dropout, GRU, Dense
from keras.callbacks import ModelCheckpoint

import matplotlib.pyplot as plt
%matplotlib inline
``````
``````Using TensorFlow backend.
``````
``````languages = [
'english',
'french',
'chinese',
'malay',
]
``````
``````examples = {}
examples['english'] = [
"one",
"two",
"three",
"eleven",
"fifteen",
"one hundred thirty-two",
"one hundred twelve",
"seven thousand eight hundred fifty-nine",
"twenty-one",
"twenty-four",
"eighty",
"ninety-one thousand",
"ninety-one thousand two hundred two",
]
examples['french'] = [
"un",
"deux",
"trois",
"onze",
"quinze",
"cent trente deux",
"cent mille douze",
"sept mille huit cent cinquante neuf",
"vingt et un",
"vingt quatre",
"quatre vingts",
"quatre vingt onze mille",
"quatre vingt onze mille deux cent deux",
]
examples['chinese'] = [
"一",
"二",
"三",
"十一",
"十五",
"一百三十二",
"十万十二",
"七千八百五十九",
"二十一",
"二十四",
"八十",
"九万一千",
"九万一千两百零二",
]
examples['malay'] = [
"satu",
"dua",
"tiga",
"sebelas",
"lima belas",
"seratus tiga puluh dua",
"seratus ribu dua belas",
"tujuh ribu lapan ratus lima puluh sembilan",
"dua puluh satu",
"dua puluh empat",
"lapan puluh",
"sembilan puluh satu ribu",
"sembilan puluh satu ribu dua ratus dua",
]
]
``````
``````PAD, GO, EOS, UNK = START_VOCAB = ['_PAD', '_GO', '_EOS', '_UNK']

def build_vocabulary(tokenized_sequences):
rev_vocabulary = START_VOCAB[:]
unique_tokens = set()
for tokens in tokenized_sequences:
unique_tokens.update(tokens)
rev_vocabulary += sorted(unique_tokens)
vocabulary = {}
for i, token in enumerate(rev_vocabulary):
vocabulary[token] = i

return vocabulary, rev_vocabulary

def make_input_output(source_tokens, target_tokens, reverse_source=True):
if reverse_source:
source_tokens = list(reversed(source_tokens))
input_tokens = source_tokens + [GO] + target_tokens
output_tokens = target_tokens + [EOS]

return input_tokens, output_tokens

def vectorize_corpus(source_sequences, target_sequences, shared_vocab,
word_level_source=True, word_level_target=True,
max_length=20):
assert len(source_sequences) == len(target_sequences)
n_sequences = len(source_sequences)
source_ids = np.empty(shape=(n_sequences, max_length), dtype=np.int32)
target_ids = np.empty(shape=(n_sequences, max_length), dtype=np.int32)
numbered_pairs = zip(range(n_sequences), source_sequences, target_sequences)
for i, source_seq, target_seq in numbered_pairs:
source_tokens = tokenize(source_seq, word_level=word_level_source)
target_tokens = tokenize(target_seq, word_level=word_level_target)

in_tokens, out_tokens = make_input_output(source_tokens, target_tokens)

in_token_ids = [shared_vocab.get(t, UNK) for t in in_tokens]
source_ids[i, -len(in_token_ids):] = in_token_ids

out_token_ids = [shared_vocab.get(t, UNK) for t in out_tokens]
target_ids[i, -len(out_token_ids):] = out_token_ids

return source_ids, target_ids

def greedy_translate(model, source_sequence, shared_vocab, rev_shared_vocab,
word_level_source=True, word_level_target=True):
"""Greedy decoder recursively predicting one token at a time"""
# Initialize the list of input token ids with the source sequence
source_tokens = tokenize(source_sequence, word_level=word_level_source)
input_ids = [shared_vocab.get(t, UNK) for t in reversed(source_tokens)]
input_ids += [shared_vocab[GO]]

# Prepare a fixed size numpy array that matches the expected input
# shape for the model
input_array = np.empty(shape=(1, model.input_shape[1]),
dtype=np.int32)
decoded_tokens = []
while len(input_ids) <= max_length:
# Vectorize a the list of input tokens as
input_array[0, -len(input_ids):] = input_ids

# Predict the next output: greedy decoding with argmax
next_token_id = model.predict(input_array)[0, -1].argmax()

# Stop decoding if the network predicts end of sentence:
if next_token_id == shared_vocab[EOS]:
break

# Otherwise use the reverse vocabulary to map the prediction
# back to the string space
decoded_tokens.append(rev_shared_vocab[next_token_id])

# Append prediction to input sequence to predict the next
input_ids.append(next_token_id)

separator = " " if word_level_target else ""

return separator.join(decoded_tokens)

def phrase_accuracy(model, num_sequences, lg_sequences, n_samples=None,
decoder_func=greedy_translate):
correct = []
n_samples = len(num_sequences) if n_samples is None else n_samples
for i, num_seq, lg_seq in zip(range(n_samples), num_sequences, lg_sequences):

predicted_seq = decoder_func(model, lg_seq,
shared_vocab, rev_shared_vocab,
word_level_target=False)
correct.append(num_seq == predicted_seq)

return np.mean(correct)
``````
``````accuracy = {}
for language in languages:
if language == 'english':
from english_numbers import generate_translations, tokenize
elif language == 'french':
from french_numbers import generate_translations, tokenize
elif language == 'chinese':
from chinese_numbers import generate_translations, tokenize
elif language == 'malay':
from malay_numbers import generate_translations, tokenize

accuracy[language] = OrderedDict()

# loop here over the size of the training set
for train_size in [500, 1000, 2000, 4000, 6000, 8000, 12000, 15000, 30000]:

tokenized_lg_train = [tokenize(s, word_level=True) for s in train['language'][:train_size]]
tokenized_num_train = [tokenize(s, word_level=False) for s in train['digits'][:train_size]]

lg_vocab, rev_lg_vocab = build_vocabulary(tokenized_lg_train)
num_vocab, rev_num_vocab = build_vocabulary(tokenized_num_train)

all_tokenized_sequences = tokenized_lg_train + tokenized_num_train
shared_vocab, rev_shared_vocab = build_vocabulary(all_tokenized_sequences)

max_length = 20
X_train, Y_train = vectorize_corpus(train['language'][:train_size], train['digits'][:train_size],
shared_vocab, word_level_target=False,
max_length=max_length)
X_validation, Y_validation = vectorize_corpus(validation['language'], validation['digits'],
shared_vocab, word_level_target=False,
max_length=max_length)
X_test, Y_test = vectorize_corpus(test['language'], test['digits'],
shared_vocab, word_level_target=False,
max_length=max_length)

vocab_size = len(shared_vocab)
simple_seq2seq = Sequential()
loss='sparse_categorical_crossentropy')

best_model_fname = "{}_simple_seq2seq_checkpoint.h5".format(language)
best_model_cb = ModelCheckpoint(best_model_fname, monitor='val_loss',
save_best_only=True, verbose=0)

history = simple_seq2seq.fit(X_train, np.expand_dims(Y_train, -1),
validation_data=(X_validation,
np.expand_dims(Y_validation, -1)),
epochs=150, verbose=0, batch_size=32,
callbacks=[best_model_cb])

plt.figure(figsize=(12, 6))
plt.plot(history.history['loss'], label='train')
plt.plot(history.history['val_loss'], '--', label='validation')
plt.ylabel('negative log likelihood')
plt.xlabel('epoch')
plt.title('Convergence plot for Simple Seq2Seq - {}'.format(language))
plt.ylim([-0.05, 1.1])
plt.show()

print("Some examples of model predictions:")
print("-----------------------------------")
for phrase in examples[language]:
translation = greedy_translate(simple_seq2seq, phrase,
shared_vocab, rev_shared_vocab,
word_level_target=False)
print(phrase.ljust(50), translation)

prediction_accuracy = phrase_accuracy(simple_seq2seq, test['digits'], test['language'])

accuracy[language][train_size] = prediction_accuracy

print("\n[{}] Phrase-level test accuracy is %0.3f when training with dataset size = {}.\n\n\n"
.format(language, train_size) % prediction_accuracy)

# display the accuracy curve as function of the train size
plt.figure(figsize=(12, 6))
plt.plot(list(accuracy[language].keys()),
list(accuracy[language].values()),
'--o')
plt.ylabel('Accuracy')
plt.xlabel('Training dataset size')
plt.title('Accuracy for learning how to translate {} numbers'.format(language))
plt.ylim([-0.05, 1.1])
plt.show()
``````

``````Some examples of model predictions:
-----------------------------------
one                                                15410
two                                                571
three                                              5710
eleven                                             11440
fifteen                                            4741
one hundred thirty-two                             15
one hundred twelve                                 1174
seven thousand eight hundred fifty-nine
twenty-one                                         8101
twenty-four                                        54
eighty                                             871
ninety-one thousand                                914
ninety-one thousand two hundred two                712

[english] Phrase-level test accuracy is 0.001 when training with dataset size = 500.
``````

``````Some examples of model predictions:
-----------------------------------
one                                                18000
two                                                4000
three                                              390
eleven                                             11006
fifteen                                            13000
one hundred thirty-two                             17
one hundred twelve                                 18012
seven thousand eight hundred fifty-nine            5859
twenty-one                                         2101
twenty-four                                        6
eighty                                             8900
ninety-one thousand                                918
ninety-one thousand two hundred two                91220

[english] Phrase-level test accuracy is 0.036 when training with dataset size = 1000.
``````

``````Some examples of model predictions:
-----------------------------------
one                                                18
two                                                2
three                                              4
eleven                                             11000
fifteen                                            85015
one hundred thirty-two                             142
one hundred twelve                                 182
seven thousand eight hundred fifty-nine            7859
twenty-one                                         21
twenty-four                                        2
eighty                                             81
ninety-one thousand                                91
ninety-one thousand two hundred two                9122

[english] Phrase-level test accuracy is 0.148 when training with dataset size = 2000.
``````

``````Some examples of model predictions:
-----------------------------------
one                                                10
two                                                20
three                                              300
eleven                                             1101
fifteen                                            15005
one hundred thirty-two                             132
one hundred twelve                                 112
seven thousand eight hundred fifty-nine            7859
twenty-one                                         214
twenty-four                                        24
eighty                                             80
ninety-one thousand                                910
ninety-one thousand two hundred two                91202

[english] Phrase-level test accuracy is 0.797 when training with dataset size = 4000.
``````

``````Some examples of model predictions:
-----------------------------------
one                                                180
two                                                20
three                                              30
one hundred thirty-two                             132
one hundred twelve                                 1212
seven thousand eight hundred fifty-nine            7959
twenty-one                                         210
twenty-four                                        24
eighty                                             80
ninety-one thousand                                91000
ninety-one thousand two hundred two                91202

[english] Phrase-level test accuracy is 0.438 when training with dataset size = 6000.
``````

``````Some examples of model predictions:
-----------------------------------
one                                                50
two                                                20
three                                              30
eleven                                             11
fifteen                                            15
one hundred thirty-two                             932
one hundred twelve                                 121
seven thousand eight hundred fifty-nine            7859
twenty-one                                         29
twenty-four                                        2
eighty                                             80
ninety-one thousand                                920
ninety-one thousand two hundred two                91002

[english] Phrase-level test accuracy is 0.218 when training with dataset size = 8000.
``````

``````Some examples of model predictions:
-----------------------------------
one                                                1
two                                                2
three                                              3
eleven                                             11
fifteen                                            15
one hundred thirty-two                             132
one hundred twelve                                 121
seven thousand eight hundred fifty-nine            7859
twenty-one                                         21
twenty-four                                        24
eighty                                             80
ninety-one thousand                                91000
ninety-one thousand two hundred two                91202

[english] Phrase-level test accuracy is 0.997 when training with dataset size = 12000.
``````

``````Some examples of model predictions:
-----------------------------------
one                                                1
two                                                2
three                                              3
eleven                                             11
fifteen                                            15
one hundred thirty-two                             132
one hundred twelve                                 112
seven thousand eight hundred fifty-nine            7859
twenty-one                                         21
twenty-four                                        24
eighty                                             80
ninety-one thousand                                91000
ninety-one thousand two hundred two                91202

[english] Phrase-level test accuracy is 0.998 when training with dataset size = 15000.
``````

``````Some examples of model predictions:
-----------------------------------
one                                                1
two                                                2
three                                              3
eleven                                             11
fifteen                                            15
one hundred thirty-two                             132
one hundred twelve                                 112
seven thousand eight hundred fifty-nine            7859
twenty-one                                         21
twenty-four                                        24
eighty                                             80
ninety-one thousand                                91000
ninety-one thousand two hundred two                91202

[english] Phrase-level test accuracy is 0.995 when training with dataset size = 30000.
``````

``````Some examples of model predictions:
-----------------------------------
un                                                 1012
deux                                               200
trois                                              510
onze                                               1112
quinze                                             191
cent trente deux                                   15
cent mille douze                                   191
sept mille huit cent cinquante neuf                766
vingt et un                                        910
vingt quatre                                       21
quatre vingts                                      90
quatre vingt onze mille                            9190
quatre vingt onze mille deux cent deux             912

[french] Phrase-level test accuracy is 0.002 when training with dataset size = 500.
``````

``````Some examples of model predictions:
-----------------------------------
un                                                 102
deux                                               2
trois                                              30
onze                                               102
quinze                                             10
cent trente deux                                   132
cent mille douze                                   132
sept mille huit cent cinquante neuf                7880
vingt et un                                        21
vingt quatre                                       2
quatre vingts
quatre vingt onze mille                            90130
quatre vingt onze mille deux cent deux             93222

[french] Phrase-level test accuracy is 0.079 when training with dataset size = 1000.
``````

``````Some examples of model predictions:
-----------------------------------
un                                                 10
deux                                               2
trois                                              3
onze                                               11
quinze                                             15
cent trente deux                                   132
cent mille douze                                   15202
sept mille huit cent cinquante neuf                9859
vingt et un                                        21
vingt quatre                                       48
quatre vingts                                      8402
quatre vingt onze mille                            91
quatre vingt onze mille deux cent deux             91202

[french] Phrase-level test accuracy is 0.285 when training with dataset size = 2000.
``````

``````Some examples of model predictions:
-----------------------------------
un                                                 1008
deux                                               200
trois                                              300
onze                                               110
quinze                                             15000
cent trente deux                                   132
cent mille douze                                   100012
sept mille huit cent cinquante neuf                7859
vingt et un                                        21
vingt quatre                                       24
quatre vingts                                      80
quatre vingt onze mille                            91
quatre vingt onze mille deux cent deux             91202

[french] Phrase-level test accuracy is 0.967 when training with dataset size = 4000.
``````

``````Some examples of model predictions:
-----------------------------------
un                                                 71
deux                                               21
trois                                              31
onze                                               11
quinze                                             150
cent trente deux                                   132
cent mille douze                                   100012
sept mille huit cent cinquante neuf                7859
vingt et un                                        20081
vingt quatre                                       20
quatre vingts                                      80
quatre vingt onze mille                            91000
quatre vingt onze mille deux cent deux             91202

[french] Phrase-level test accuracy is 0.997 when training with dataset size = 6000.
``````

``````Some examples of model predictions:
-----------------------------------
un                                                 10
deux                                               20
trois                                              31
onze                                               11
quinze                                             55005
cent trente deux                                   132
cent mille douze                                   100012
sept mille huit cent cinquante neuf                7859
vingt et un                                        25
vingt quatre                                       24
quatre vingts                                      80
quatre vingt onze mille                            91
quatre vingt onze mille deux cent deux             91202

[french] Phrase-level test accuracy is 0.999 when training with dataset size = 8000.
``````

``````Some examples of model predictions:
-----------------------------------
un                                                 1
deux                                               2102
trois                                              3032
onze                                               10
quinze                                             15
cent trente deux                                   132
cent mille douze                                   100012
sept mille huit cent cinquante neuf                7859
vingt et un                                        22
vingt quatre                                       24
quatre vingts                                      80
quatre vingt onze mille                            91000
quatre vingt onze mille deux cent deux             91202

[french] Phrase-level test accuracy is 0.999 when training with dataset size = 12000.
``````

``````Some examples of model predictions:
-----------------------------------
un                                                 1
deux                                               2
trois                                              3
onze                                               11
quinze                                             15
cent trente deux                                   132
cent mille douze                                   100012
sept mille huit cent cinquante neuf                7859
vingt et un                                        27
vingt quatre                                       24
quatre vingts                                      80
quatre vingt onze mille                            91000
quatre vingt onze mille deux cent deux             91202

[french] Phrase-level test accuracy is 0.999 when training with dataset size = 15000.
``````

``````Some examples of model predictions:
-----------------------------------
un                                                 1
deux                                               2
trois                                              3
onze                                               11
quinze                                             15
cent trente deux                                   132
cent mille douze                                   100012
sept mille huit cent cinquante neuf                7859
vingt et un                                        21
vingt quatre                                       24
quatre vingts                                      80
quatre vingt onze mille                            91000
quatre vingt onze mille deux cent deux             91202

[french] Phrase-level test accuracy is 1.000 when training with dataset size = 30000.
``````

``````Some examples of model predictions:
-----------------------------------

[chinese] Phrase-level test accuracy is 0.113 when training with dataset size = 500.
``````

``````Some examples of model predictions:
-----------------------------------

[chinese] Phrase-level test accuracy is 0.413 when training with dataset size = 1000.
``````

``````Some examples of model predictions:
-----------------------------------

[chinese] Phrase-level test accuracy is 0.929 when training with dataset size = 2000.
``````

``````Some examples of model predictions:
-----------------------------------

[chinese] Phrase-level test accuracy is 0.995 when training with dataset size = 4000.
``````

``````Some examples of model predictions:
-----------------------------------

[chinese] Phrase-level test accuracy is 0.997 when training with dataset size = 6000.
``````

``````Some examples of model predictions:
-----------------------------------

[chinese] Phrase-level test accuracy is 0.999 when training with dataset size = 8000.
``````

``````Some examples of model predictions:
-----------------------------------

[chinese] Phrase-level test accuracy is 0.999 when training with dataset size = 12000.
``````

``````Some examples of model predictions:
-----------------------------------

[chinese] Phrase-level test accuracy is 1.000 when training with dataset size = 15000.
``````

``````Some examples of model predictions:
-----------------------------------

[chinese] Phrase-level test accuracy is 1.000 when training with dataset size = 30000.
``````

``````Some examples of model predictions:
-----------------------------------
satu                                               1174
dua                                                5
tiga                                               6
sebelas                                            1144
lima belas                                         1514
seratus tiga puluh dua                             159
seratus ribu dua belas                             1052
tujuh ribu lapan ratus lima puluh sembilan         5653
dua puluh satu                                     214
dua puluh empat                                    57
lapan puluh                                        87
sembilan puluh satu ribu                           9171
sembilan puluh satu ribu dua ratus dua             9125

[malay] Phrase-level test accuracy is 0.012 when training with dataset size = 500.
``````

``````Some examples of model predictions:
-----------------------------------
satu                                               110
dua                                                31
tiga                                               3
sebelas                                            1110
lima belas                                         110
seratus tiga puluh dua                             13
seratus ribu dua belas                             1012
tujuh ribu lapan ratus lima puluh sembilan         7667
dua puluh satu                                     210
dua puluh empat                                    24
lapan puluh                                        87
sembilan puluh satu ribu                           910
sembilan puluh satu ribu dua ratus dua             972

[malay] Phrase-level test accuracy is 0.028 when training with dataset size = 1000.
``````

``````Some examples of model predictions:
-----------------------------------
satu                                               100
dua                                                200
tiga                                               100
sebelas                                            110
lima belas                                         151
seratus tiga puluh dua                             122
seratus ribu dua belas                             192
tujuh ribu lapan ratus lima puluh sembilan         7859
dua puluh satu                                     210
dua puluh empat                                    24
lapan puluh                                        8108
sembilan puluh satu ribu                           9108
sembilan puluh satu ribu dua ratus dua             9222

[malay] Phrase-level test accuracy is 0.098 when training with dataset size = 2000.
``````

``````Some examples of model predictions:
-----------------------------------
satu                                               101
dua                                                500
tiga                                               005
sebelas                                            111
lima belas                                         1501
seratus tiga puluh dua                             132
seratus ribu dua belas                             1012
tujuh ribu lapan ratus lima puluh sembilan         7859
dua puluh satu                                     210
dua puluh empat                                    204
lapan puluh                                        800
sembilan puluh satu ribu                           91
sembilan puluh satu ribu dua ratus dua             91202

[malay] Phrase-level test accuracy is 0.466 when training with dataset size = 4000.
``````

``````Some examples of model predictions:
-----------------------------------
satu                                               1010
dua                                                20
tiga                                               30
sebelas                                            1111
lima belas                                         1511
seratus tiga puluh dua                             132
seratus ribu dua belas                             10012
tujuh ribu lapan ratus lima puluh sembilan         7859
dua puluh satu                                     21
dua puluh empat                                    20
lapan puluh                                        80
sembilan puluh satu ribu                           91000
sembilan puluh satu ribu dua ratus dua             91202

[malay] Phrase-level test accuracy is 0.964 when training with dataset size = 6000.
``````

``````Some examples of model predictions:
-----------------------------------
satu                                               106
dua                                                20
tiga                                               30
sebelas                                            1111
lima belas                                         15
seratus tiga puluh dua                             138
seratus ribu dua belas                             100012
tujuh ribu lapan ratus lima puluh sembilan         7859
dua puluh satu                                     21
dua puluh empat                                    24
lapan puluh                                        80
sembilan puluh satu ribu                           91000
sembilan puluh satu ribu dua ratus dua             91202

[malay] Phrase-level test accuracy is 0.995 when training with dataset size = 8000.
``````

``````Some examples of model predictions:
-----------------------------------
satu                                               1
dua                                                20
tiga                                               30
sebelas                                            110
lima belas                                         15
seratus tiga puluh dua                             132
seratus ribu dua belas                             100012
tujuh ribu lapan ratus lima puluh sembilan         7859
dua puluh satu                                     20
dua puluh empat                                    24
lapan puluh                                        80
sembilan puluh satu ribu                           91000
sembilan puluh satu ribu dua ratus dua             91202

[malay] Phrase-level test accuracy is 0.999 when training with dataset size = 12000.
``````

``````Some examples of model predictions:
-----------------------------------
satu                                               1
dua                                                200
tiga                                               3
sebelas                                            1
lima belas                                         15
seratus tiga puluh dua                             132
seratus ribu dua belas                             100012
tujuh ribu lapan ratus lima puluh sembilan         7859
dua puluh satu                                     21
dua puluh empat                                    24
lapan puluh                                        80
sembilan puluh satu ribu                           91000
sembilan puluh satu ribu dua ratus dua             91202

[malay] Phrase-level test accuracy is 0.980 when training with dataset size = 15000.
``````

``````Some examples of model predictions:
-----------------------------------
satu                                               1
dua                                                2
tiga                                               3
sebelas                                            11
lima belas                                         15
seratus tiga puluh dua                             132
seratus ribu dua belas                             100012
tujuh ribu lapan ratus lima puluh sembilan         7859
dua puluh satu                                     21
dua puluh empat                                    24
lapan puluh                                        80
sembilan puluh satu ribu                           91000
sembilan puluh satu ribu dua ratus dua             91202

[malay] Phrase-level test accuracy is 1.000 when training with dataset size = 30000.
``````

``````Some examples of model predictions:
-----------------------------------
1                                                  17
2                                                  17
3                                                  13
B                                                  12
F                                                  12
84                                                 17
186AC
1EB3                                               1
15                                                 17
18                                                 17
50                                                 13
16378                                              1
16442

[hexadecimal] Phrase-level test accuracy is 0.000 when training with dataset size = 500.
``````

``````Some examples of model predictions:
-----------------------------------
1
2
3
B
F
84
186AC
1EB3
15
18
50
16378
16442

[hexadecimal] Phrase-level test accuracy is 0.000 when training with dataset size = 1000.
``````

``````Some examples of model predictions:
-----------------------------------
1                                                  3
2                                                  82
3                                                  13717
B                                                  2
F                                                  2
84                                                 2128
186AC
1EB3
15                                                 3
18                                                 23
50                                                 2
16378
16442

[hexadecimal] Phrase-level test accuracy is 0.000 when training with dataset size = 2000.
``````

``````Some examples of model predictions:
-----------------------------------
1
2
3                                                  B
B
F
84
186AC
1EB3
15
18
50
16378
16442

[hexadecimal] Phrase-level test accuracy is 0.000 when training with dataset size = 4000.
``````

``````Some examples of model predictions:
-----------------------------------
1
2
3
B
F
84
186AC
1EB3
15
18
50
16378
16442

[hexadecimal] Phrase-level test accuracy is 0.000 when training with dataset size = 6000.
``````

``````Some examples of model predictions:
-----------------------------------
1
2
3
B
F
84
186AC
1EB3
15
18
50
16378
16442

[hexadecimal] Phrase-level test accuracy is 0.000 when training with dataset size = 8000.
``````

``````Some examples of model predictions:
-----------------------------------
1                                                  6
2                                                  9
3                                                  2
B                                                  4
F                                                  6
84                                                 3
186AC
1EB3
15
18
50                                                 2
16378
16442

[hexadecimal] Phrase-level test accuracy is 0.000 when training with dataset size = 12000.
``````

``````Some examples of model predictions:
-----------------------------------
1                                                  1
2                                                  70
3                                                  7
B                                                  1
F                                                  4
84                                                 2
186AC
1EB3                                               457
15
18
50                                                 6
16378
16442

[hexadecimal] Phrase-level test accuracy is 0.000 when training with dataset size = 15000.
``````

``````Some examples of model predictions:
-----------------------------------
1                                                  1
2                                                  7
3                                                  6
B                                                  1
F                                                  2
84
186AC
1EB3
15                                                 3
18                                                 6
50                                                 8
16378
16442

[hexadecimal] Phrase-level test accuracy is 0.000 when training with dataset size = 30000.
``````

``````# display the accuracy curve as function of the train size
plt.figure(figsize=(12, 6))
for language in languages:
plt.plot(list(accuracy[language].keys()),
list(accuracy[language].values()),
'--o',
label=language)
plt.ylabel('Accuracy')
plt.xlabel('Training dataset size')
plt.title('Accuracy for learning how to translate numbers')
plt.ylim([-0.05, 1.1])
plt.legend()
plt.show()
``````