.. _sec_bert-dataset:
BERTの事前学習のためのデータセット
==================================
:numref:`sec_bert` で実装した BERT モデルを事前学習するには、
2つの事前学習タスク、すなわち
マスク付き言語モデルと次文予測を容易にするための、
理想的な形式のデータセットを生成する必要がある。 一方で、 元の BERT
モデルは BookCorpus と English Wikipedia という
2つの巨大なコーパスの連結上で事前学習されており
(:numref:`subsec_bert_pretraining_tasks` を参照)、
本書の多くの読者にとっては実行が難しいである。 他方で、
既製の事前学習済み BERT モデルは、
医療のような特定分野のアプリケーションには適さない場合がある。
そのため、カスタマイズしたデータセット上で BERT を事前学習することが
一般的になりつつある。 BERT の事前学習を示しやすくするために、
ここではより小さなコーパスである WikiText-2
:cite:`Merity.Xiong.Bradbury.ea.2016` を用いる。
:numref:`sec_word2vec_data` で word2vec の事前学習に用いた PTB
データセットと比べると、 WikiText-2 は (i)
元の句読点を保持しているため、次文予測に適している、 (ii)
元の大文字小文字と数値を保持している、 (iii)
2倍以上大きい、という特徴がある。
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\diilbookstyleinputcell
.. code:: python
from d2l import torch as d2l
import os
import random
import torch
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\diilbookstyleinputcell
.. code:: python
from d2l import mxnet as d2l
from mxnet import gluon, np, npx
import os
import random
npx.set_np()
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\diilbookstyleinputcell
.. code:: python
#@save
d2l.DATA_HUB['wikitext-2'] = (
'https://s3.amazonaws.com/research.metamind.io/wikitext/'
'wikitext-2-v1.zip', '3c914d17d80b1459be871a5039ac23e752a53cbe')
#@save
def _read_wiki(data_dir):
file_name = os.path.join(data_dir, 'wiki.train.tokens')
with open(file_name, 'r') as f:
lines = f.readlines()
# Uppercase letters are converted to lowercase ones
paragraphs = [line.strip().lower().split(' . ')
for line in lines if len(line.split(' . ')) >= 2]
random.shuffle(paragraphs)
return paragraphs
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\diilbookstyleinputcell
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#@save
d2l.DATA_HUB['wikitext-2'] = (
'https://s3.amazonaws.com/research.metamind.io/wikitext/'
'wikitext-2-v1.zip', '3c914d17d80b1459be871a5039ac23e752a53cbe')
#@save
def _read_wiki(data_dir):
file_name = os.path.join(data_dir, 'wiki.train.tokens')
with open(file_name, 'r') as f:
lines = f.readlines()
# Uppercase letters are converted to lowercase ones
paragraphs = [line.strip().lower().split(' . ')
for line in lines if len(line.split(' . ')) >= 2]
random.shuffle(paragraphs)
return paragraphs
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\diilbookstyleinputcell
.. code:: python
#@save
d2l.DATA_HUB['wikitext-2'] = (
'https://s3.amazonaws.com/research.metamind.io/wikitext/'
'wikitext-2-v1.zip', '3c914d17d80b1459be871a5039ac23e752a53cbe')
#@save
def _read_wiki(data_dir):
file_name = os.path.join(data_dir, 'wiki.train.tokens')
with open(file_name, 'r') as f:
lines = f.readlines()
# Uppercase letters are converted to lowercase ones
paragraphs = [line.strip().lower().split(' . ')
for line in lines if len(line.split(' . ')) >= 2]
random.shuffle(paragraphs)
return paragraphs
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\diilbookstyleinputcell
.. code:: python
#@save
d2l.DATA_HUB['wikitext-2'] = (
'https://s3.amazonaws.com/research.metamind.io/wikitext/'
'wikitext-2-v1.zip', '3c914d17d80b1459be871a5039ac23e752a53cbe')
#@save
def _read_wiki(data_dir):
file_name = os.path.join(data_dir, 'wiki.train.tokens')
with open(file_name, 'r') as f:
lines = f.readlines()
# Uppercase letters are converted to lowercase ones
paragraphs = [line.strip().lower().split(' . ')
for line in lines if len(line.split(' . ')) >= 2]
random.shuffle(paragraphs)
return paragraphs
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\diilbookstyleinputcell
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#@save
def _get_next_sentence(sentence, next_sentence, paragraphs):
if random.random() < 0.5:
is_next = True
else:
# `paragraphs` is a list of lists of lists
next_sentence = random.choice(random.choice(paragraphs))
is_next = False
return sentence, next_sentence, is_next
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\diilbookstyleinputcell
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#@save
def _get_next_sentence(sentence, next_sentence, paragraphs):
if random.random() < 0.5:
is_next = True
else:
# `paragraphs` is a list of lists of lists
next_sentence = random.choice(random.choice(paragraphs))
is_next = False
return sentence, next_sentence, is_next
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\diilbookstyleinputcell
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#@save
def _get_next_sentence(sentence, next_sentence, paragraphs):
if random.random() < 0.5:
is_next = True
else:
# `paragraphs` is a list of lists of lists
next_sentence = random.choice(random.choice(paragraphs))
is_next = False
return sentence, next_sentence, is_next
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\diilbookstyleinputcell
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#@save
def _get_next_sentence(sentence, next_sentence, paragraphs):
if random.random() < 0.5:
is_next = True
else:
# `paragraphs` is a list of lists of lists
next_sentence = random.choice(random.choice(paragraphs))
is_next = False
return sentence, next_sentence, is_next
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\diilbookstyleinputcell
.. code:: python
#@save
def _get_nsp_data_from_paragraph(paragraph, paragraphs, vocab, max_len):
nsp_data_from_paragraph = []
for i in range(len(paragraph) - 1):
tokens_a, tokens_b, is_next = _get_next_sentence(
paragraph[i], paragraph[i + 1], paragraphs)
# Consider 1 '' token and 2 '' tokens
if len(tokens_a) + len(tokens_b) + 3 > max_len:
continue
tokens, segments = d2l.get_tokens_and_segments(tokens_a, tokens_b)
nsp_data_from_paragraph.append((tokens, segments, is_next))
return nsp_data_from_paragraph
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#@save
def _get_nsp_data_from_paragraph(paragraph, paragraphs, vocab, max_len):
nsp_data_from_paragraph = []
for i in range(len(paragraph) - 1):
tokens_a, tokens_b, is_next = _get_next_sentence(
paragraph[i], paragraph[i + 1], paragraphs)
# Consider 1 '' token and 2 '' tokens
if len(tokens_a) + len(tokens_b) + 3 > max_len:
continue
tokens, segments = d2l.get_tokens_and_segments(tokens_a, tokens_b)
nsp_data_from_paragraph.append((tokens, segments, is_next))
return nsp_data_from_paragraph
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\diilbookstyleinputcell
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#@save
def _get_nsp_data_from_paragraph(paragraph, paragraphs, vocab, max_len):
nsp_data_from_paragraph = []
for i in range(len(paragraph) - 1):
tokens_a, tokens_b, is_next = _get_next_sentence(
paragraph[i], paragraph[i + 1], paragraphs)
# Consider 1 '' token and 2 '' tokens
if len(tokens_a) + len(tokens_b) + 3 > max_len:
continue
tokens, segments = d2l.get_tokens_and_segments(tokens_a, tokens_b)
nsp_data_from_paragraph.append((tokens, segments, is_next))
return nsp_data_from_paragraph
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\diilbookstyleinputcell
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#@save
def _get_nsp_data_from_paragraph(paragraph, paragraphs, vocab, max_len):
nsp_data_from_paragraph = []
for i in range(len(paragraph) - 1):
tokens_a, tokens_b, is_next = _get_next_sentence(
paragraph[i], paragraph[i + 1], paragraphs)
# Consider 1 '' token and 2 '' tokens
if len(tokens_a) + len(tokens_b) + 3 > max_len:
continue
tokens, segments = d2l.get_tokens_and_segments(tokens_a, tokens_b)
nsp_data_from_paragraph.append((tokens, segments, is_next))
return nsp_data_from_paragraph
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\diilbookstyleinputcell
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#@save
def _replace_mlm_tokens(tokens, candidate_pred_positions, num_mlm_preds,
vocab):
# For the input of a masked language model, make a new copy of tokens and
# replace some of them by '' or random tokens
mlm_input_tokens = [token for token in tokens]
pred_positions_and_labels = []
# Shuffle for getting 15% random tokens for prediction in the masked
# language modeling task
random.shuffle(candidate_pred_positions)
for mlm_pred_position in candidate_pred_positions:
if len(pred_positions_and_labels) >= num_mlm_preds:
break
masked_token = None
# 80% of the time: replace the word with the '' token
if random.random() < 0.8:
masked_token = ''
else:
# 10% of the time: keep the word unchanged
if random.random() < 0.5:
masked_token = tokens[mlm_pred_position]
# 10% of the time: replace the word with a random word
else:
masked_token = random.choice(vocab.idx_to_token)
mlm_input_tokens[mlm_pred_position] = masked_token
pred_positions_and_labels.append(
(mlm_pred_position, tokens[mlm_pred_position]))
return mlm_input_tokens, pred_positions_and_labels
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\diilbookstyleinputcell
.. code:: python
#@save
def _replace_mlm_tokens(tokens, candidate_pred_positions, num_mlm_preds,
vocab):
# For the input of a masked language model, make a new copy of tokens and
# replace some of them by '' or random tokens
mlm_input_tokens = [token for token in tokens]
pred_positions_and_labels = []
# Shuffle for getting 15% random tokens for prediction in the masked
# language modeling task
random.shuffle(candidate_pred_positions)
for mlm_pred_position in candidate_pred_positions:
if len(pred_positions_and_labels) >= num_mlm_preds:
break
masked_token = None
# 80% of the time: replace the word with the '' token
if random.random() < 0.8:
masked_token = ''
else:
# 10% of the time: keep the word unchanged
if random.random() < 0.5:
masked_token = tokens[mlm_pred_position]
# 10% of the time: replace the word with a random word
else:
masked_token = random.choice(vocab.idx_to_token)
mlm_input_tokens[mlm_pred_position] = masked_token
pred_positions_and_labels.append(
(mlm_pred_position, tokens[mlm_pred_position]))
return mlm_input_tokens, pred_positions_and_labels
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\diilbookstyleinputcell
.. code:: python
#@save
def _replace_mlm_tokens(tokens, candidate_pred_positions, num_mlm_preds,
vocab):
# For the input of a masked language model, make a new copy of tokens and
# replace some of them by '' or random tokens
mlm_input_tokens = [token for token in tokens]
pred_positions_and_labels = []
# Shuffle for getting 15% random tokens for prediction in the masked
# language modeling task
random.shuffle(candidate_pred_positions)
for mlm_pred_position in candidate_pred_positions:
if len(pred_positions_and_labels) >= num_mlm_preds:
break
masked_token = None
# 80% of the time: replace the word with the '' token
if random.random() < 0.8:
masked_token = ''
else:
# 10% of the time: keep the word unchanged
if random.random() < 0.5:
masked_token = tokens[mlm_pred_position]
# 10% of the time: replace the word with a random word
else:
masked_token = random.choice(vocab.idx_to_token)
mlm_input_tokens[mlm_pred_position] = masked_token
pred_positions_and_labels.append(
(mlm_pred_position, tokens[mlm_pred_position]))
return mlm_input_tokens, pred_positions_and_labels
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\diilbookstyleinputcell
.. code:: python
#@save
def _replace_mlm_tokens(tokens, candidate_pred_positions, num_mlm_preds,
vocab):
# For the input of a masked language model, make a new copy of tokens and
# replace some of them by '' or random tokens
mlm_input_tokens = [token for token in tokens]
pred_positions_and_labels = []
# Shuffle for getting 15% random tokens for prediction in the masked
# language modeling task
random.shuffle(candidate_pred_positions)
for mlm_pred_position in candidate_pred_positions:
if len(pred_positions_and_labels) >= num_mlm_preds:
break
masked_token = None
# 80% of the time: replace the word with the '' token
if random.random() < 0.8:
masked_token = ''
else:
# 10% of the time: keep the word unchanged
if random.random() < 0.5:
masked_token = tokens[mlm_pred_position]
# 10% of the time: replace the word with a random word
else:
masked_token = random.choice(vocab.idx_to_token)
mlm_input_tokens[mlm_pred_position] = masked_token
pred_positions_and_labels.append(
(mlm_pred_position, tokens[mlm_pred_position]))
return mlm_input_tokens, pred_positions_and_labels
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\diilbookstyleinputcell
.. code:: python
#@save
def _get_mlm_data_from_tokens(tokens, vocab):
candidate_pred_positions = []
# `tokens` is a list of strings
for i, token in enumerate(tokens):
# Special tokens are not predicted in the masked language modeling
# task
if token in ['', '']:
continue
candidate_pred_positions.append(i)
# 15% of random tokens are predicted in the masked language modeling task
num_mlm_preds = max(1, round(len(tokens) * 0.15))
mlm_input_tokens, pred_positions_and_labels = _replace_mlm_tokens(
tokens, candidate_pred_positions, num_mlm_preds, vocab)
pred_positions_and_labels = sorted(pred_positions_and_labels,
key=lambda x: x[0])
pred_positions = [v[0] for v in pred_positions_and_labels]
mlm_pred_labels = [v[1] for v in pred_positions_and_labels]
return vocab[mlm_input_tokens], pred_positions, vocab[mlm_pred_labels]
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\diilbookstyleinputcell
.. code:: python
#@save
def _get_mlm_data_from_tokens(tokens, vocab):
candidate_pred_positions = []
# `tokens` is a list of strings
for i, token in enumerate(tokens):
# Special tokens are not predicted in the masked language modeling
# task
if token in ['', '']:
continue
candidate_pred_positions.append(i)
# 15% of random tokens are predicted in the masked language modeling task
num_mlm_preds = max(1, round(len(tokens) * 0.15))
mlm_input_tokens, pred_positions_and_labels = _replace_mlm_tokens(
tokens, candidate_pred_positions, num_mlm_preds, vocab)
pred_positions_and_labels = sorted(pred_positions_and_labels,
key=lambda x: x[0])
pred_positions = [v[0] for v in pred_positions_and_labels]
mlm_pred_labels = [v[1] for v in pred_positions_and_labels]
return vocab[mlm_input_tokens], pred_positions, vocab[mlm_pred_labels]
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\diilbookstyleinputcell
.. code:: python
#@save
def _get_mlm_data_from_tokens(tokens, vocab):
candidate_pred_positions = []
# `tokens` is a list of strings
for i, token in enumerate(tokens):
# Special tokens are not predicted in the masked language modeling
# task
if token in ['', '']:
continue
candidate_pred_positions.append(i)
# 15% of random tokens are predicted in the masked language modeling task
num_mlm_preds = max(1, round(len(tokens) * 0.15))
mlm_input_tokens, pred_positions_and_labels = _replace_mlm_tokens(
tokens, candidate_pred_positions, num_mlm_preds, vocab)
pred_positions_and_labels = sorted(pred_positions_and_labels,
key=lambda x: x[0])
pred_positions = [v[0] for v in pred_positions_and_labels]
mlm_pred_labels = [v[1] for v in pred_positions_and_labels]
return vocab[mlm_input_tokens], pred_positions, vocab[mlm_pred_labels]
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\diilbookstyleinputcell
.. code:: python
#@save
def _get_mlm_data_from_tokens(tokens, vocab):
candidate_pred_positions = []
# `tokens` is a list of strings
for i, token in enumerate(tokens):
# Special tokens are not predicted in the masked language modeling
# task
if token in ['', '']:
continue
candidate_pred_positions.append(i)
# 15% of random tokens are predicted in the masked language modeling task
num_mlm_preds = max(1, round(len(tokens) * 0.15))
mlm_input_tokens, pred_positions_and_labels = _replace_mlm_tokens(
tokens, candidate_pred_positions, num_mlm_preds, vocab)
pred_positions_and_labels = sorted(pred_positions_and_labels,
key=lambda x: x[0])
pred_positions = [v[0] for v in pred_positions_and_labels]
mlm_pred_labels = [v[1] for v in pred_positions_and_labels]
return vocab[mlm_input_tokens], pred_positions, vocab[mlm_pred_labels]
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\diilbookstyleinputcell
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batch_size, max_len = 512, 64
train_iter, vocab = load_data_wiki(batch_size, max_len)
for (tokens_X, segments_X, valid_lens_x, pred_positions_X, mlm_weights_X,
mlm_Y, nsp_y) in train_iter:
print(tokens_X.shape, segments_X.shape, valid_lens_x.shape,
pred_positions_X.shape, mlm_weights_X.shape, mlm_Y.shape,
nsp_y.shape)
break
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batch_size, max_len = 512, 64
train_iter, vocab = load_data_wiki(batch_size, max_len)
for (tokens_X, segments_X, valid_lens_x, pred_positions_X, mlm_weights_X,
mlm_Y, nsp_y) in train_iter:
print(tokens_X.shape, segments_X.shape, valid_lens_x.shape,
pred_positions_X.shape, mlm_weights_X.shape, mlm_Y.shape,
nsp_y.shape)
break
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\diilbookstyleinputcell
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#@save
def _pad_bert_inputs(examples, max_len, vocab):
max_num_mlm_preds = round(max_len * 0.15)
all_token_ids, all_segments, valid_lens, = [], [], []
all_pred_positions, all_mlm_weights, all_mlm_labels = [], [], []
nsp_labels = []
for (token_ids, pred_positions, mlm_pred_label_ids, segments,
is_next) in examples:
all_token_ids.append(torch.tensor(token_ids + [vocab['
']] * (
max_len - len(token_ids)), dtype=torch.long))
all_segments.append(torch.tensor(segments + [0] * (
max_len - len(segments)), dtype=torch.long))
# `valid_lens` excludes count of '' tokens
valid_lens.append(torch.tensor(len(token_ids), dtype=torch.float32))
all_pred_positions.append(torch.tensor(pred_positions + [0] * (
max_num_mlm_preds - len(pred_positions)), dtype=torch.long))
# Predictions of padded tokens will be filtered out in the loss via
# multiplication of 0 weights
all_mlm_weights.append(
torch.tensor([1.0] * len(mlm_pred_label_ids) + [0.0] * (
max_num_mlm_preds - len(pred_positions)),
dtype=torch.float32))
all_mlm_labels.append(torch.tensor(mlm_pred_label_ids + [0] * (
max_num_mlm_preds - len(mlm_pred_label_ids)), dtype=torch.long))
nsp_labels.append(torch.tensor(is_next, dtype=torch.long))
return (all_token_ids, all_segments, valid_lens, all_pred_positions,
all_mlm_weights, all_mlm_labels, nsp_labels)
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\diilbookstyleinputcell
.. code:: python
#@save
def _pad_bert_inputs(examples, max_len, vocab):
max_num_mlm_preds = round(max_len * 0.15)
all_token_ids, all_segments, valid_lens, = [], [], []
all_pred_positions, all_mlm_weights, all_mlm_labels = [], [], []
nsp_labels = []
for (token_ids, pred_positions, mlm_pred_label_ids, segments,
is_next) in examples:
all_token_ids.append(np.array(token_ids + [vocab['
']] * (
max_len - len(token_ids)), dtype='int32'))
all_segments.append(np.array(segments + [0] * (
max_len - len(segments)), dtype='int32'))
# `valid_lens` excludes count of '' tokens
valid_lens.append(np.array(len(token_ids), dtype='float32'))
all_pred_positions.append(np.array(pred_positions + [0] * (
max_num_mlm_preds - len(pred_positions)), dtype='int32'))
# Predictions of padded tokens will be filtered out in the loss via
# multiplication of 0 weights
all_mlm_weights.append(
np.array([1.0] * len(mlm_pred_label_ids) + [0.0] * (
max_num_mlm_preds - len(pred_positions)), dtype='float32'))
all_mlm_labels.append(np.array(mlm_pred_label_ids + [0] * (
max_num_mlm_preds - len(mlm_pred_label_ids)), dtype='int32'))
nsp_labels.append(np.array(is_next))
return (all_token_ids, all_segments, valid_lens, all_pred_positions,
all_mlm_weights, all_mlm_labels, nsp_labels)
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\diilbookstyleinputcell
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len(vocab)
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\diilbookstyleinputcell
.. code:: python
len(vocab)
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\diilbookstyleinputcell
.. code:: python
#@save
class _WikiTextDataset(torch.utils.data.Dataset):
def __init__(self, paragraphs, max_len):
# Input `paragraphs[i]` is a list of sentence strings representing a
# paragraph; while output `paragraphs[i]` is a list of sentences
# representing a paragraph, where each sentence is a list of tokens
paragraphs = [d2l.tokenize(
paragraph, token='word') for paragraph in paragraphs]
sentences = [sentence for paragraph in paragraphs
for sentence in paragraph]
self.vocab = d2l.Vocab(sentences, min_freq=5, reserved_tokens=[
'
', '', '', ''])
# Get data for the next sentence prediction task
examples = []
for paragraph in paragraphs:
examples.extend(_get_nsp_data_from_paragraph(
paragraph, paragraphs, self.vocab, max_len))
# Get data for the masked language model task
examples = [(_get_mlm_data_from_tokens(tokens, self.vocab)
+ (segments, is_next))
for tokens, segments, is_next in examples]
# Pad inputs
(self.all_token_ids, self.all_segments, self.valid_lens,
self.all_pred_positions, self.all_mlm_weights,
self.all_mlm_labels, self.nsp_labels) = _pad_bert_inputs(
examples, max_len, self.vocab)
def __getitem__(self, idx):
return (self.all_token_ids[idx], self.all_segments[idx],
self.valid_lens[idx], self.all_pred_positions[idx],
self.all_mlm_weights[idx], self.all_mlm_labels[idx],
self.nsp_labels[idx])
def __len__(self):
return len(self.all_token_ids)
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\diilbookstyleinputcell
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#@save
class _WikiTextDataset(gluon.data.Dataset):
def __init__(self, paragraphs, max_len):
# Input `paragraphs[i]` is a list of sentence strings representing a
# paragraph; while output `paragraphs[i]` is a list of sentences
# representing a paragraph, where each sentence is a list of tokens
paragraphs = [d2l.tokenize(
paragraph, token='word') for paragraph in paragraphs]
sentences = [sentence for paragraph in paragraphs
for sentence in paragraph]
self.vocab = d2l.Vocab(sentences, min_freq=5, reserved_tokens=[
'
', '', '', ''])
# Get data for the next sentence prediction task
examples = []
for paragraph in paragraphs:
examples.extend(_get_nsp_data_from_paragraph(
paragraph, paragraphs, self.vocab, max_len))
# Get data for the masked language model task
examples = [(_get_mlm_data_from_tokens(tokens, self.vocab)
+ (segments, is_next))
for tokens, segments, is_next in examples]
# Pad inputs
(self.all_token_ids, self.all_segments, self.valid_lens,
self.all_pred_positions, self.all_mlm_weights,
self.all_mlm_labels, self.nsp_labels) = _pad_bert_inputs(
examples, max_len, self.vocab)
def __getitem__(self, idx):
return (self.all_token_ids[idx], self.all_segments[idx],
self.valid_lens[idx], self.all_pred_positions[idx],
self.all_mlm_weights[idx], self.all_mlm_labels[idx],
self.nsp_labels[idx])
def __len__(self):
return len(self.all_token_ids)
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#@save
def load_data_wiki(batch_size, max_len):
"""Load the WikiText-2 dataset."""
num_workers = d2l.get_dataloader_workers()
data_dir = d2l.download_extract('wikitext-2', 'wikitext-2')
paragraphs = _read_wiki(data_dir)
train_set = _WikiTextDataset(paragraphs, max_len)
train_iter = torch.utils.data.DataLoader(train_set, batch_size,
shuffle=True, num_workers=num_workers)
return train_iter, train_set.vocab
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#@save
def load_data_wiki(batch_size, max_len):
"""Load the WikiText-2 dataset."""
num_workers = d2l.get_dataloader_workers()
data_dir = d2l.download_extract('wikitext-2', 'wikitext-2')
paragraphs = _read_wiki(data_dir)
train_set = _WikiTextDataset(paragraphs, max_len)
train_iter = gluon.data.DataLoader(train_set, batch_size, shuffle=True,
num_workers=num_workers)
return train_iter, train_set.vocab
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