# 15.7. Natural Language Inference: Fine-Tuning BERT¶ Open the notebook in Colab Open the notebook in Colab

In earlier sections of this chapter, we have designed an attention-based architecture (in Section 15.5) for the natural language inference task on the SNLI dataset (as described in Section 15.4). Now we revisit this task by fine-tuning BERT. As discussed in Section 15.6, natural language inference is a sequence-level text pair classification problem, and fine-tuning BERT only requires an additional MLP-based architecture, as illustrated in Fig. 15.7.1.

Fig. 15.7.1 This section feeds pretrained BERT to an MLP-based architecture for natural language inference.

In this section, we will download a pretrained small version of BERT, then fine-tune it for natural language inference on the SNLI dataset.

from d2l import mxnet as d2l
import json
import multiprocessing
from mxnet import autograd, gluon, init, np, npx
from mxnet.gluon import nn
import os

npx.set_np()


We have explained how to pretrain BERT on the WikiText-2 dataset in Section 14.9 and Section 14.10 (note that the original BERT model is pretrained on much bigger corpora). As discussed in Section 14.10, the original BERT model has hundreds of millions of parameters. In the following, we provide two versions of pretrained BERT: “bert.base” is about as big as the original BERT base model that requires a lot of computational resources to fine-tune, while “bert.small” is a small version to facilitate demonstration.

d2l.DATA_HUB['bert.base'] = (d2l.DATA_URL + 'bert.base.zip',
'7b3820b35da691042e5d34c0971ac3edbd80d3f4')
d2l.DATA_HUB['bert.small'] = (d2l.DATA_URL + 'bert.small.zip',
'a4e718a47137ccd1809c9107ab4f5edd317bae2c')


Either pretrained BERT model contains a “vocab.json” file that defines the vocabulary set and a “pretrained.params” file of the pretrained parameters. We implement the following load_pretrained_model function to load pretrained BERT parameters.

def load_pretrained_model(pretrained_model, num_hiddens, ffn_num_hiddens,
# Define an empty vocabulary to load the predefined vocabulary
vocab = d2l.Vocab([])
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)
return bert, vocab


To facilitate demonstration on most of machines, we will load and fine-tune the small version (“bert.small”) of the pretrained BERT in this section. In the exercise, we will show how to fine-tune the much larger “bert.base” to significantly improve the testing accuracy.

ctx = d2l.try_all_gpus()
num_layers=2, dropout=0.1, max_len=512, ctx=ctx)

Downloading ../data/bert.small.zip from http://d2l-data.s3-accelerate.amazonaws.com/bert.small.zip...


## 15.7.2. The Dataset for Fine-Tuning BERT¶

For the downstream task natural language inference on the SNLI dataset, we define a customized dataset class SNLIBERTDataset. In each example, the premise and hypothesis form a pair of text sequence and is packed into one BERT input sequence as depicted in Fig. 15.6.2. Recall Section 14.8.4 that segment IDs are used to distinguish the premise and the hypothesis in a BERT input sequence. With the predefined maximum length of a BERT input sequence (max_len), the last token of the longer of the input text pair keeps getting removed until max_len is met. To accelerate generation of the SNLI dataset for fine-tuning BERT, we use 4 worker processes to generate training or testing examples in parallel.

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['<pad>']] \
* (self.max_len - len(tokens))
segments = segments + [0] * (self.max_len - len(segments))
valid_len = len(tokens)

def _truncate_pair_of_tokens(self, p_tokens, h_tokens):
# Reserve slots for '<CLS>', '<SEP>', and '<SEP>' 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)


After downloading the SNLI dataset, we generate training and testing examples by instantiating the SNLIBERTDataset class. Such examples will be read in minibatches during training and testing of natural language inference.

# 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()
train_set = SNLIBERTDataset(d2l.read_snli(data_dir, True), max_len, vocab)
test_set = SNLIBERTDataset(d2l.read_snli(data_dir, False), max_len, vocab)
num_workers=num_workers)
num_workers=num_workers)

read 549367 examples


## 15.7.3. Fine-Tuning BERT¶

As Fig. 15.6.2 indicates, fine-tuning BERT for natural language inference requires only an extra MLP consisting of two fully-connected layers (see self.hidden and self.output in the following BERTClassifier class). This MLP transforms the BERT representation of the special “<cls>” token, which encodes the information of both the premise and the hypothesis, into three outputs of natural language inference: entailment, contradiction, and neutral.

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, :]))


In the following, the pretrained BERT model bert is fed into the BERTClassifier instance net for the downstream application. In common implementations of BERT fine-tuning, only the parameters of the output layer of the additional MLP (net.output) will be learned from scratch. All the parameters of the pretrained BERT encoder (net.encoder) and the hidden layer of the additional MLP (net.hidden) will be fine-tuned.

net = BERTClassifier(bert)
net.output.initialize(ctx=ctx)


Recall that in Section 14.8 both the MaskLM class and the NextSentencePred class have parameters in their employed MLPs. These parameters are part of those in the pretrained BERT model bert, and thus part of parameters in net. However, such parameters are only for computing the masked language modeling loss and the next sentence prediction loss during pretraining. These two loss functions are irrelevant to fine-tuning downstream applications, thus the parameters of the employed MLPs in MaskLM and NextSentencePred are not updated (staled) when BERT is fine-tuned.

To allow parameters with stale gradients, the flag ignore_stale_grad=True is set in the step function of d2l.train_batch_ch13. We use this function to train and evaluate the model net using the training set (train_iter) and the testing set (test_iter) of SNLI. Due to the limited computational resources, the training and testing accuracy can be further improved: we leave its discussions in the exercises.

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, ctx,
d2l.split_batch_multi_inputs)

loss 0.597, train acc 0.741, test acc 0.713
8563.9 examples/sec on [gpu(0), gpu(1)]


## 15.7.4. Summary¶

• We can fine-tune the pretrained BERT model for downstream applications, such as natural language inference on the SNLI dataset.

• During fine-tuning, the BERT model becomes part of the model for the downstream application. Parameters that are only related to pretraining loss will not be updated during fine-tuning.

## 15.7.5. Exercises¶

1. Fine-tune a much larger pretrained BERT model that is about as big as the original BERT base model if your computational resource allows. Set arguments in the load_pretrained_model function as: replacing ‘bert.small’ with ‘bert.base’, increasing values of num_hiddens=256, ffn_num_hiddens=512, num_heads=4, num_layers=2 to 768, 3072, 12, 12, respectively. By increasing fine-tuning epochs (and possibly tuning other hyperparameters), can you get a testing accuracy higher than 0.86?

2. How to truncate a pair of sequences according to their ratio of length? Compare this pair truncation method and the one used in the SNLIBERTDataset class. What are their pros and cons?