.. _sec_natural-language-inference-bert:
Inferência de Linguagem Natural: Ajuste Fino do BERT
====================================================
Nas seções anteriores deste capítulo, nós projetamos uma arquitetura
baseada na atenção (em
:numref:`sec_natural-language-inference-attention`) para a tarefa de
inferência de linguagem natural no conjunto de dados SNLI (conforme
descrito em :numref:`sec_natural-language-inference-and-dataset`).
Agora, revisitamos essa tarefa fazendo o ajuste fino do BERT. Conforme
discutido em :numref:`sec_finetuning-bert`, a inferência de linguagem
natural é um problema de classificação de pares de texto em nível de
sequência, e o ajuste fino de BERT requer apenas uma arquitetura
adicional baseada em MLP, conforme ilustrado em
:numref:`fig_nlp-map-nli-bert`.
.. _fig_nlp-map-nli-bert:
.. figure:: ../img/nlp-map-nli-bert.svg
Esta seção alimenta BERT pré-treinado para uma arquitetura baseada em
MLP para inferência de linguagem natural.
Nesta secção, vamos baixar uma versão pequena pré-treinada de BERT,
então ajuste-o para inferência de linguagem natural no conjunto de dados
SNLI.
.. raw:: html
.. raw:: html
.. code:: python
import json
import multiprocessing
import os
from mxnet import gluon, np, npx
from mxnet.gluon import nn
from d2l import mxnet as d2l
npx.set_np()
.. raw:: html
.. raw:: html
.. code:: python
import json
import multiprocessing
import os
import torch
from torch import nn
from d2l import torch as d2l
.. raw:: html
.. raw:: html
.. raw:: html
.. code:: python
d2l.DATA_HUB['bert.base'] = (d2l.DATA_URL + 'bert.base.zip',
'7b3820b35da691042e5d34c0971ac3edbd80d3f4')
d2l.DATA_HUB['bert.small'] = (d2l.DATA_URL + 'bert.small.zip',
'a4e718a47137ccd1809c9107ab4f5edd317bae2c')
.. raw:: html
.. raw:: html
.. code:: python
d2l.DATA_HUB['bert.base'] = (d2l.DATA_URL + 'bert.base.torch.zip',
'225d66f04cae318b841a13d32af3acc165f253ac')
d2l.DATA_HUB['bert.small'] = (d2l.DATA_URL + 'bert.small.torch.zip',
'c72329e68a732bef0452e4b96a1c341c8910f81f')
.. raw:: html
.. raw:: html
.. raw:: html
.. code:: python
def load_pretrained_model(pretrained_model, num_hiddens, ffn_num_hiddens,
num_heads, num_layers, dropout, max_len, devices):
data_dir = d2l.download_extract(pretrained_model)
# Define an empty vocabulary to load the predefined vocabulary
vocab = d2l.Vocab()
vocab.idx_to_token = json.load(open(os.path.join(data_dir, 'vocab.json')))
vocab.token_to_idx = {token: idx for idx, token in enumerate(
vocab.idx_to_token)}
bert = d2l.BERTModel(len(vocab), num_hiddens, ffn_num_hiddens, num_heads,
num_layers, dropout, max_len)
# Load pretrained BERT parameters
bert.load_parameters(os.path.join(data_dir, 'pretrained.params'),
ctx=devices)
return bert, vocab
.. raw:: html
.. raw:: html
.. code:: python
def load_pretrained_model(pretrained_model, num_hiddens, ffn_num_hiddens,
num_heads, num_layers, dropout, max_len, devices):
data_dir = d2l.download_extract(pretrained_model)
# Define an empty vocabulary to load the predefined vocabulary
vocab = d2l.Vocab()
vocab.idx_to_token = json.load(open(os.path.join(data_dir, 'vocab.json')))
vocab.token_to_idx = {token: idx for idx, token in enumerate(
vocab.idx_to_token)}
bert = d2l.BERTModel(len(vocab), num_hiddens, norm_shape=[256],
ffn_num_input=256, ffn_num_hiddens=ffn_num_hiddens,
num_heads=4, num_layers=2, dropout=0.2,
max_len=max_len, key_size=256, query_size=256,
value_size=256, hid_in_features=256,
mlm_in_features=256, nsp_in_features=256)
# Load pretrained BERT parameters
bert.load_state_dict(torch.load(os.path.join(data_dir,
'pretrained.params')))
return bert, vocab
.. raw:: html
.. raw:: html
.. raw:: html
.. code:: python
devices = d2l.try_all_gpus()
bert, vocab = load_pretrained_model(
'bert.small', num_hiddens=256, ffn_num_hiddens=512, num_heads=4,
num_layers=2, dropout=0.1, max_len=512, devices=devices)
.. raw:: html
.. raw:: html
.. code:: python
devices = d2l.try_all_gpus()
bert, vocab = load_pretrained_model(
'bert.small', num_hiddens=256, ffn_num_hiddens=512, num_heads=4,
num_layers=2, dropout=0.1, max_len=512, devices=devices)
.. parsed-literal::
:class: output
Downloading ../data/bert.small.torch.zip from http://d2l-data.s3-accelerate.amazonaws.com/bert.small.torch.zip...
.. raw:: html
.. raw:: html
.. raw:: html
.. code:: python
class SNLIBERTDataset(gluon.data.Dataset):
def __init__(self, dataset, max_len, vocab=None):
all_premise_hypothesis_tokens = [[
p_tokens, h_tokens] for p_tokens, h_tokens in zip(
*[d2l.tokenize([s.lower() for s in sentences])
for sentences in dataset[:2]])]
self.labels = np.array(dataset[2])
self.vocab = vocab
self.max_len = max_len
(self.all_token_ids, self.all_segments,
self.valid_lens) = self._preprocess(all_premise_hypothesis_tokens)
print('read ' + str(len(self.all_token_ids)) + ' examples')
def _preprocess(self, all_premise_hypothesis_tokens):
pool = multiprocessing.Pool(4) # Use 4 worker processes
out = pool.map(self._mp_worker, all_premise_hypothesis_tokens)
all_token_ids = [
token_ids for token_ids, segments, valid_len in out]
all_segments = [segments for token_ids, segments, valid_len in out]
valid_lens = [valid_len for token_ids, segments, valid_len in out]
return (np.array(all_token_ids, dtype='int32'),
np.array(all_segments, dtype='int32'),
np.array(valid_lens))
def _mp_worker(self, premise_hypothesis_tokens):
p_tokens, h_tokens = premise_hypothesis_tokens
self._truncate_pair_of_tokens(p_tokens, h_tokens)
tokens, segments = d2l.get_tokens_and_segments(p_tokens, h_tokens)
token_ids = self.vocab[tokens] + [self.vocab['
']] \
* (self.max_len - len(tokens))
segments = segments + [0] * (self.max_len - len(segments))
valid_len = len(tokens)
return token_ids, segments, valid_len
def _truncate_pair_of_tokens(self, p_tokens, h_tokens):
# Reserve slots for '', '', and '' tokens for the BERT
# input
while len(p_tokens) + len(h_tokens) > self.max_len - 3:
if len(p_tokens) > len(h_tokens):
p_tokens.pop()
else:
h_tokens.pop()
def __getitem__(self, idx):
return (self.all_token_ids[idx], self.all_segments[idx],
self.valid_lens[idx]), self.labels[idx]
def __len__(self):
return len(self.all_token_ids)
.. raw:: html
.. raw:: html
.. code:: python
class SNLIBERTDataset(torch.utils.data.Dataset):
def __init__(self, dataset, max_len, vocab=None):
all_premise_hypothesis_tokens = [[
p_tokens, h_tokens] for p_tokens, h_tokens in zip(
*[d2l.tokenize([s.lower() for s in sentences])
for sentences in dataset[:2]])]
self.labels = torch.tensor(dataset[2])
self.vocab = vocab
self.max_len = max_len
(self.all_token_ids, self.all_segments,
self.valid_lens) = self._preprocess(all_premise_hypothesis_tokens)
print('read ' + str(len(self.all_token_ids)) + ' examples')
def _preprocess(self, all_premise_hypothesis_tokens):
pool = multiprocessing.Pool(4) # Use 4 worker processes
out = pool.map(self._mp_worker, all_premise_hypothesis_tokens)
all_token_ids = [
token_ids for token_ids, segments, valid_len in out]
all_segments = [segments for token_ids, segments, valid_len in out]
valid_lens = [valid_len for token_ids, segments, valid_len in out]
return (torch.tensor(all_token_ids, dtype=torch.long),
torch.tensor(all_segments, dtype=torch.long),
torch.tensor(valid_lens))
def _mp_worker(self, premise_hypothesis_tokens):
p_tokens, h_tokens = premise_hypothesis_tokens
self._truncate_pair_of_tokens(p_tokens, h_tokens)
tokens, segments = d2l.get_tokens_and_segments(p_tokens, h_tokens)
token_ids = self.vocab[tokens] + [self.vocab['
']] \
* (self.max_len - len(tokens))
segments = segments + [0] * (self.max_len - len(segments))
valid_len = len(tokens)
return token_ids, segments, valid_len
def _truncate_pair_of_tokens(self, p_tokens, h_tokens):
# Reserve slots for '', '', and '' tokens for the BERT
# input
while len(p_tokens) + len(h_tokens) > self.max_len - 3:
if len(p_tokens) > len(h_tokens):
p_tokens.pop()
else:
h_tokens.pop()
def __getitem__(self, idx):
return (self.all_token_ids[idx], self.all_segments[idx],
self.valid_lens[idx]), self.labels[idx]
def __len__(self):
return len(self.all_token_ids)
.. raw:: html
.. raw:: html
.. raw:: html
.. code:: python
# Reduce `batch_size` if there is an out of memory error. In the original BERT
# model, `max_len` = 512
batch_size, max_len, num_workers = 512, 128, d2l.get_dataloader_workers()
data_dir = d2l.download_extract('SNLI')
train_set = SNLIBERTDataset(d2l.read_snli(data_dir, True), max_len, vocab)
test_set = SNLIBERTDataset(d2l.read_snli(data_dir, False), max_len, vocab)
train_iter = gluon.data.DataLoader(train_set, batch_size, shuffle=True,
num_workers=num_workers)
test_iter = gluon.data.DataLoader(test_set, batch_size,
num_workers=num_workers)
.. parsed-literal::
:class: output
read 549367 examples
read 9824 examples
.. raw:: html
.. raw:: html
.. code:: python
# Reduce `batch_size` if there is an out of memory error. In the original BERT
# model, `max_len` = 512
batch_size, max_len, num_workers = 512, 128, d2l.get_dataloader_workers()
data_dir = d2l.download_extract('SNLI')
train_set = SNLIBERTDataset(d2l.read_snli(data_dir, True), max_len, vocab)
test_set = SNLIBERTDataset(d2l.read_snli(data_dir, False), max_len, vocab)
train_iter = torch.utils.data.DataLoader(train_set, batch_size, shuffle=True,
num_workers=num_workers)
test_iter = torch.utils.data.DataLoader(test_set, batch_size,
num_workers=num_workers)
.. parsed-literal::
:class: output
read 549367 examples
read 9824 examples
.. raw:: html
.. raw:: html
.. raw:: html
.. code:: python
class BERTClassifier(nn.Block):
def __init__(self, bert):
super(BERTClassifier, self).__init__()
self.encoder = bert.encoder
self.hidden = bert.hidden
self.output = nn.Dense(3)
def forward(self, inputs):
tokens_X, segments_X, valid_lens_x = inputs
encoded_X = self.encoder(tokens_X, segments_X, valid_lens_x)
return self.output(self.hidden(encoded_X[:, 0, :]))
.. raw:: html
.. raw:: html
.. code:: python
class BERTClassifier(nn.Module):
def __init__(self, bert):
super(BERTClassifier, self).__init__()
self.encoder = bert.encoder
self.hidden = bert.hidden
self.output = nn.Linear(256, 3)
def forward(self, inputs):
tokens_X, segments_X, valid_lens_x = inputs
encoded_X = self.encoder(tokens_X, segments_X, valid_lens_x)
return self.output(self.hidden(encoded_X[:, 0, :]))
.. raw:: html
.. raw:: html
.. raw:: html
.. code:: python
net = BERTClassifier(bert)
net.output.initialize(ctx=devices)
.. raw:: html
.. raw:: html
.. code:: python
net = BERTClassifier(bert)
.. raw:: html
.. raw:: html
.. raw:: html
.. code:: python
lr, num_epochs = 1e-4, 5
trainer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': lr})
loss = gluon.loss.SoftmaxCrossEntropyLoss()
d2l.train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs, devices,
d2l.split_batch_multi_inputs)
.. parsed-literal::
:class: output
loss 0.479, train acc 0.810, test acc 0.787
7970.3 examples/sec on [gpu(0), gpu(1)]
.. figure:: output_natural-language-inference-bert_1857e6_75_1.svg
.. raw:: html
.. raw:: html
.. code:: python
lr, num_epochs = 1e-4, 5
trainer = torch.optim.Adam(net.parameters(), lr=lr)
loss = nn.CrossEntropyLoss(reduction='none')
d2l.train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs, devices)
.. parsed-literal::
:class: output
loss 0.518, train acc 0.791, test acc 0.777
8488.3 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]
.. figure:: output_natural-language-inference-bert_1857e6_78_1.svg
.. raw:: html
.. raw:: html
.. raw:: html
`Discussões `__
.. raw:: html
.. raw:: html
`Discussões `__
.. raw:: html
.. raw:: html