Building Language Models: A Step-by-Step BERT Implementation Guide

Advances in machine learning models that process language have been rapid in the last few years. This progress has left the research lab and is beginning to power some leading digital products. A great example is the announcement that BERT models are now a significant force behind Google Search. Google believes that this move ( advances in natural language understanding applied to search) represents “the biggest jump in the past five years and one of the biggest in the history of search.” Let’s understand what is BERT? So , in this Article you will get to know about the BERT Implementation Guide and Why we need it, how does it work and either various things you will get to know in this Guide.

BERT stands for Bidirectional Encoder Representations from Transformers. Its design involves pre-training deep bidirectional representations from the unlabeled text, conditioning on both the left and right contexts. We can enhance the pre-trained BERT model for different NLP tasks by adding just one additional output layer.

In this article, you will discover how to use the BERT model effectively for natural language processing tasks, enhancing your understanding of its applications and benefits.

Learning  objectives

• Understand the architecture and components of BERT.
• Learn the preprocessing steps required for BERT input and how to handle varying input sequence lengths.
• Gain practical knowledge of implementing BERT using popular machine learning frameworks like TensorFlow or PyTorch.
• Learn how to fine-tune BERT for specific downstream tasks, such as text classification or named entity recognition.

Now another question will be coming why do we need that? Let me explain.

This article was published as a part of the Data Science Blogathon.

Why Do We Need BERT?

Proper language representation is the ability of machines to grasp the general language. Context-free models like word2Vec or Glove generate a single word embedding representation for each word in the vocabulary. For example, the term “crane” would have the exact representation in “crane in the sky” and in “crane to lift heavy objects.” Contextual models represent each word based on the other words in the sentence. So BERT is a contextual model which captures these relationships bidirectionally.

BERT builds upon recent work and clever ideas in pre-training contextual representations, including Semi-supervised Sequence Learning, Generative Pre-Training, ELMo, the OpenAI Transformer, ULMFit, and the Transformer. Although these models are all unidirectional or shallowly bidirectional, BERT is fully bidirectional.

We may train the BERT models on our data for a specific purpose, such as sentiment analysis or question answering, to provide advanced predictions, or we can use them to extract high-quality language features from our text data. The next question that comes to mind is, “What’s going on behind it?” Let’s move on to understand this.

What is the Core Idea Behind it?

To understand the ideas first, we need to know about a few things such as:-

• What is language modeling?
• Which problem are language models trying to solve?

Let’s take one example: Fill in the blank based on context to understand this.

A language model(One-Directional Approach) will complete this sentence by saying that the words:

• cart
• pair

Most respondents (80%) will choose pair, while 20% will select cart right. Both are legitimate, but which should I take into consideration? Select the appropriate word to fill in the blank using the various techniques.

Now BERT comes into the picture, a bi-directionally trained language model. This means we have a more profound sense of language context than single-direction language models.

Moreover, BERT is based on the Transformer model architecture instead of LSTMs.

What is BERT?

BERT, or Bidirectional Encoder Representations from Transformers, stands as a pivotal milestone in natural language processing (NLP). Introduced by Google AI in 2018, BERT revolutionized NLP by its ability to capture contextual information bidirectionally. Unlike its predecessors, which read text in one direction, BERT comprehends words in sentences by considering both their left and right context. This capability greatly enhances its understanding of nuances in language, making it highly effective in various NLP tasks.

BERT’s architecture, based on the Transformer model, involves training on massive text corpora, resulting in a versatile and context-aware language model. Its applications span a wide range of NLP tasks, including sentiment analysis, text classification, question answering, and language understanding. Researchers and developers frequently fine-tune BERT for specific tasks, further leveraging its pre-trained capabilities to achieve state-of-the-art results across various domains. In essence, BERT has become a cornerstone tool in modern NLP, significantly advancing the accuracy and sophistication of language understanding and generation systems.

BERT’s Architecture

BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based language model architecture. It consists of multiple layers of self-attention and feed-forward neural networks. BERT utilizes a bidirectional approach to capture contextual information from preceding and following words in a sentence. There are four types of pre-trained versions of BERT depending on the scale of the model architecture:

1) BERT-Base (Cased / Un-Cased): 12-layer, 768-hidden-nodes, 12-attention-heads, 110M parameters

2) BERT-Large (Cased / Un-Cased): 24-layer, 1024-hidden-nodes, 16-attention-heads, 340M parameters

As per your requirement, you can select BERT’s pre-trained weights. For example, we will move forward with base models if we don’t have access to Google TPU. And then, the choice of “cased” vs. “uncased” depends on whether letter casing will be helpful for the task at hand. Let’s Dive into it.

How Does it Work?

BERT works by leveraging the power of unsupervised pre-training followed by supervised fine-tuning. This section will convert two areas: text preprocessing and pre-training tasks.

Text Preprocessing

A fundamental Transformer consists of an encoder for reading text input and a decoder for producing a task prediction. There is only a need for the encoder element of BERT because its goal is to create a language representation model. The input to the BERT encoder is a stream of tokens first converted into vectors. Then the neural network processes them.

To begin with, each input embedding combines the following three embeddings:

Add the token, segmentation, and position embeddings together to form the input representation for BERT.

• Token Embeddings: At the start of the first sentence, a [CLS] token is added to the input word tokens, and after each sentence, a [SEP] token is added.
• Embeddings of Segments: Each token receives a marking designating Sentence A or Sentence B. Because of this, the encoder can tell which sentences are which.
• Positional Embeddings: Each token is has a positional embedding to show where it belongs in the sentence.

Pre-Training Tasks

BERT has already completed two NLP tasks:

1. Modeling Masked Language

Predicting the next word from a string of words is the job of language modeling. In masked language modeling, some input tokens are randomly masked, and only those masked tokens are predicted rather than the token that comes after it.

• Token [MASK]: This token indicates that another token is missing.
• The masked token [MASK] is not always used to replace the masked words because, in that case, the masked tokens would never be shown before fine-tuning. Thus, a random selection is made for 15% of the tokens. In addition, of the 15% of tokens chosen for masking:

2. Next Sentence Prediction

The following sentence prediction task assesses whether the second sentence in a pair genuinely follows the first sentence. A binary classification problem exists.

Constructing this work from any monolingual corpus is easy. Recognizing the connection between two sentences is beneficial as it is necessary for various downstream tasks like Question and Answering and Natural Language Inference.

What is BERT used for?

BERT is a powerful language model architecture that can be used for a wide variety of natural language processing (NLP) tasks, including:

• Text classification: BERT can be used to classify text into different categories, such as spam/not spam, positive/negative, or factual/opinion.
• Question answering: It can be used to answer questions about a given text passage.
• Natural language inference: It can be used to determine whether a hypothesis is true or false given a premise.
• Machine translation: It can be used to translate text from one language to another.
• Text summarization: It can be used to summarize long pieces of text into shorter, more concise versions.

Implementation of BERT

Implementing BERT (Bidirectional Encoder Representations from Transformers) involves utilizing pre-trained BERT models and fine-tuning them on the specific task. This includes tokenizing the text data, encoding sequences, defining the model architecture, training the model, and evaluating its performance. BERT’s implementation offers powerful language modeling capabilities, allowing for influential natural language processing tasks such as text classification and sentiment analysis. Here’s a list of steps for implementing BERT:

• Import Required Libraries & Dataset
• Split the Dataset into train/test
• Import BERT – base- uncased
• Tokenize & Encode the Sequences
• List to Tensors
• Data Loader
• Model Architecture
• Fine – Tune
• Make Predictions

Let’s start with the problem statement.

Problem Statement

The objective is to create a system that can classify SMS messages as spam or non-spam. This system aims to improve user experience and prevent potential security threats by accurately identifying and filtering out spam messages. The task involves developing a model distinguishing between spam and legitimate texts, enabling prompt detection and action against unwanted messages.

We have several SMS messages, which is the problem. The majority of these emails are authentic. However, some of them are spam. Our goal is to create a system that can instantly determine whether or not a text is spam. Dataset Link:- ()

Import Required Libraries & Dataset

Imports the necessary libraries and datasets for the task at hand. It prepares the environment by loading the required dependencies and makes the dataset available for further processing and analysis.

import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
import transformers
from transformers import AutoModel, BertTokenizerFast

# specify GPU
device = torch.device("cuda")

df = pd.read_csv("../input/spamdatatest/spamdata_v2.csv")
df.head()

The dataset consists of two columns – “label” and “text.” The column “text” contains the message body, and the “label” is a binary variable where 1 means spam and 0 represents the message that is not spam.

# check class distribution
df['label'].value_counts(normalize = True)

Split the Dataset into Train/Test

dividing a dataset for trains into train, validation, and test sets.

We divide the dataset into three parts based on the given parameters using a library like scikit-learn’s train_test_split function.

The resulting sets, namely train_text, val_text, and test_text, are accompanied by their respective labels: train_labels, val_labels, and test_labels. These sets can be utilized for training, validating, and testing the machine learning model.

Evaluating model performance on hypothetical data makes it possible to assess models and avoid overfitting properly.

# split train dataset into train, validation and test sets
train_text, temp_text, train_labels, temp_labels = train_test_split(df['text'], df['label'], 
                                                                    random_state=2018, 
                                                                    test_size=0.3, 
                                                                    stratify=df['label'])


val_text, test_text, val_labels, test_labels = train_test_split(temp_text, temp_labels, 
                                                                random_state=2018, 
                                                                test_size=0.5, 
                                                                stratify=temp_labels)

Import BERT-Base-Uncased

The BERT-base pre-trained model is imported using the AutoModel.from_pretrained() function from the Hugging Face Transformers library. This allows users to access the BERT architecture and its pre-trained weights for powerful language processing tasks.

The BERT tokenizer is also loaded using the BertTokenizerFast.from_pretrained() function. The tokenizer is responsible for converting input text into tokens that BERT understands. The ‘Bert-base-uncased’ tokenizer is specifically designed for handling lowercase text and is aligned with the ‘Bert-base-uncased’ pre-trained model.

# import BERT-base pretrained model
bert = AutoModel.from_pretrained('bert-base-uncased')

# Load the BERT tokenizer
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')

# get length of all the messages in the train set
seq_len = [len(i.split()) for i in train_text]

pd.Series(seq_len).hist(bins = 30)

Tokenize & Encode the Sequences

How does BERT implement tokenization?

For tokenization, BERT uses WordPiece.

We initialize the vocabulary with all the individual characters in the language and then iteratively update it with the most frequent/likely combinations of the existing words.

To maintain consistency, the input sequence length is restricted to 512 characters.

We utilize the BERT tokenizer to tokenize and encode the sequences in the training, validation, and test sets. By employing the tokenizer.batch_encode_plus() function, the text sequences are transformed into numerical tokens.

For uniformity in sequence length, a maximum length of 25 is established for each set. When the pad_to_max_length=True parameter is set, the sequences are padded or truncated accordingly. Sequences longer than the specified maximum length are truncated when the truncation=True parameter is enabled.

# tokenize and encode sequences in the training set
tokens_train = tokenizer.batch_encode_plus(
    train_text.tolist(),
    max_length = 25,
    pad_to_max_length=True,
    truncation=True
)

# tokenize and encode sequences in the validation set
tokens_val = tokenizer.batch_encode_plus(
    val_text.tolist(),
    max_length = 25,
    pad_to_max_length=True,
    truncation=True
)

# tokenize and encode sequences in the test set
tokens_test = tokenizer.batch_encode_plus(
    test_text.tolist(),
    max_length = 25,
    pad_to_max_length=True,
    truncation=True
)

 List to Tensors

To convert the tokenized sequences and corresponding labels into tensors using PyTorch. The “torch. tensor()” function creates tensors from the tokenized sequences and labels.

For each set (training, validation, and test), the tokenized input sequences are converted to tensors using “torch. tensor(tokens_train[‘input_ids’])”. Similarly, the attention masks are converted to tensors using a “torch. tensor(tokens_train[‘attention_mask’])”. Convert the labels to tensors utilizing a torch.tensor(train_labels.tolist()).

Converting the data to tensors allows for efficient computation and compatibility with PyTorch models, enabling further processing and training using BERT or other models in the PyTorch ecosystem.

## convert lists to tensors

train_seq = torch.tensor(tokens_train[‘input_ids’])
train_mask = torch.tensor(tokens_train[‘attention_mask’])
train_y = torch.tensor(train_labels.tolist())

val_seq = torch.tensor(tokens_val[‘input_ids’])
val_mask = torch.tensor(tokens_val[‘attention_mask’])
val_y = torch.tensor(val_labels.tolist())

test_seq = torch.tensor(tokens_test[‘input_ids’])
test_mask = torch.tensor(tokens_test[‘attention_mask’])
test_y = torch.tensor(test_labels.tolist())

Data Loader

The creation of data loaders using PyTorch’s TensorDataset, DataLoader, RandomSampler, and SequentialSampler classes. The TensorDataset class wraps the input sequences, attention masks, and labels into a single dataset object.

We use the RandomSampler to randomly sample the training set, ensuring diverse data representation during training. Conversely, we employ the SequentialSampler for the validation set to sequentially test the data.

To facilitate efficient iteration and batching of the data during training and validation, we employ the DataLoader. This tool enables the creation of iterators over the datasets with a designated batch size, streamlining the process.

from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler

#define a batch size
batch_size = 32

# wrap tensors
train_data = TensorDataset(train_seq, train_mask, train_y)

# sampler for sampling the data during training
train_sampler = RandomSampler(train_data)

# dataLoader for train set
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)

# wrap tensors
val_data = TensorDataset(val_seq, val_mask, val_y)

# sampler for sampling the data during training
val_sampler = SequentialSampler(val_data)

# dataLoader for validation set
val_dataloader = DataLoader(val_data, sampler = val_sampler, batch_size=batch_size)

Model Architecture

The BERT_Arch class extends the nn.Module class and initializes the BERT model as a parameter.
By setting the parameters of the BERT model not to require gradients (param.requires_grad = False), we ensure that only the parameters of the added layers are trained during the training process. This technique allows us to leverage the pre-trained BERT model for transfer learning and adapt it to a specific task.

# freeze all the parameters
for param in bert.parameters():
    param.requires_grad = False

The architecture consists of a dropout layer, a ReLU activation function, two dense layers (with 768 and 512 units, respectively), and a softmax activation function. The forward method takes sentence IDs and masks as inputs, passes them through the BERT model to obtain the output from the classification token (cls_hs), and then applies the defined layers and activations to produce the final classification probabilities.

class BERT_Arch(nn.Module):

    def __init__(self, bert):
        super(BERT_Arch, self).__init__()
        
        self.bert = bert 
        
        # dropout layer
        self.dropout = nn.Dropout(0.1)
      
        # relu activation function
        self.relu =  nn.ReLU()

        # dense layer 1
        self.fc1 = nn.Linear(768,512)
      
        # dense layer 2 (Output layer)
        self.fc2 = nn.Linear(512,2)

        #softmax activation function
        self.softmax = nn.LogSoftmax(dim=1)

    #define the forward pass
    def forward(self, sent_id, mask):
        
        #pass the inputs to the model  
        _, cls_hs = self.bert(sent_id, attention_mask=mask, return_dict=False)
      
        x = self.fc1(cls_hs)

        x = self.relu(x)

        x = self.dropout(x)

        # output layer
        x = self.fc2(x)
      
        # apply softmax activation
        x = self.softmax(x)

        return x

To initialize an instance of the BERT_Arch class with the BERT model as an argument, we pass the pre-trained BERT model to the defined architecture, BERT_Arch. This establishes the BERT model as the backbone of the custom architecture.

GPU Acceleration

The model is moved to the GPU by calling the to() method and specifying the desired device (device) to leverage GPU acceleration. This allows for faster computations during training and inference by utilizing the parallel processing capabilities of the GPU.

# pass the pre-trained BERT to our define architecture
model = BERT_Arch(bert)

# push the model to GPU
model = model.to(device)

The AdamW optimizer from the Hugging Face import the Transformers library. AdamW is a variant of the Adam optimizer that includes weight decay regularization.

The optimizer is then defined by passing the model parameters (model. parameters()) and the learning rate (lr) of 1e-5 to the AdamW optimizer constructor. This optimizer will update the model parameters during training, optimizing the model’s performance on the task at hand.

# optimizer from hugging face transformers
from transformers import AdamW

# define the optimizer
optimizer = AdamW(model.parameters(),lr = 1e-5)

The compute_class_weight function from the sklearn.utils.class_weight module is used to compute the class weights with multiple parameters for the training labels.

from sklearn.utils.class_weight import compute_class_weight

#compute the class weights
class_weights = compute_class_weight(‘balanced’, np.unique(train_labels), train_labels)

print(“Class Weights:”,class_weights)

To convert the class weights to a tensor, move it to the GPU and defines the loss function with weighted class weights. The number of training epochs is set to 10.

# converting list of class weights to a tensor
weights= torch.tensor(class_weights,dtype=torch.float)

# push to GPU
weights = weights.to(device)

# define the loss function
cross_entropy  = nn.NLLLoss(weight=weights) 

# number of training epochs
epochs = 10

Fine-Tune

A training function that iterates over batches of data performs forward and backward passes, updates model parameters and computes the training loss. The function also stores the model predictions and returns the average loss and predictions.

# function to train the model
def train():
    
    model.train()
    total_loss, total_accuracy = 0, 0
  
    # empty list to save model predictions
    total_preds=[]
  
    # iterate over batches
    for step,batch in enumerate(train_dataloader):
        
        # progress update after every 50 batches.
        if step % 50 == 0 and not step == 0:
            print('  Batch {:>5,}  of  {:>5,}.'.format(step, len(train_dataloader)))
        
        # push the batch to gpu
        batch = [r.to(device) for r in batch]
 
        sent_id, mask, labels = batch
        
        # clear previously calculated gradients 
        model.zero_grad()        

        # get model predictions for the current batch
        preds = model(sent_id, mask)

        # compute the loss between actual and predicted values
        loss = cross_entropy(preds, labels)

        # add on to the total loss
        total_loss = total_loss + loss.item()

        # backward pass to calculate the gradients
        loss.backward()

        # clip the the gradients to 1.0. It helps in preventing the exploding gradient problem
        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)

        # update parameters
        optimizer.step()

        # model predictions are stored on GPU. So, push it to CPU
        preds=preds.detach().cpu().numpy()

    # append the model predictions
    total_preds.append(preds)

    # compute the training loss of the epoch
    avg_loss = total_loss / len(train_dataloader)
  
      # predictions are in the form of (no. of batches, size of batch, no. of classes).
      # reshape the predictions in form of (number of samples, no. of classes)
    total_preds  = np.concatenate(total_preds, axis=0)

    #returns the loss and predictions
    return avg_loss, total_preds

An evaluation function that evaluates the model on the validation data. It computes the validation loss, stores the model predictions, and returns the average loss and predictions. The function deactivates dropout layers and performs forward passes without gradient computation using torch.no_grad().

# function for evaluating the model
def evaluate():
    
    print("\nEvaluating...")
  
    # deactivate dropout layers
    model.eval()

    total_loss, total_accuracy = 0, 0
    
    # empty list to save the model predictions
    total_preds = []

    # iterate over batches
    for step,batch in enumerate(val_dataloader):
        
        # Progress update every 50 batches.
        if step % 50 == 0 and not step == 0:
            
            # Calculate elapsed time in minutes.
            elapsed = format_time(time.time() - t0)
            
            # Report progress.
            print('  Batch {:>5,}  of  {:>5,}.'.format(step, len(val_dataloader)))

        # push the batch to gpu
        batch = [t.to(device) for t in batch]

        sent_id, mask, labels = batch

        # deactivate autograd
        with torch.no_grad():
            
            # model predictions
            preds = model(sent_id, mask)

            # compute the validation loss between actual and predicted values
            loss = cross_entropy(preds,labels)

            total_loss = total_loss + loss.item()

            preds = preds.detach().cpu().numpy()

            total_preds.append(preds)

    # compute the validation loss of the epoch
    avg_loss = total_loss / len(val_dataloader) 

    # reshape the predictions in form of (number of samples, no. of classes)
    total_preds  = np.concatenate(total_preds, axis=0)

    return avg_loss, total_preds

Train the Model

To train the model for the specified number of epochs. It tracks the best validation loss, saves the model weights if the current validation loss is better, and appends the training and validation losses to their respective lists. The training and validation losses are printed for each epoch.

# set initial loss to infinite
best_valid_loss = float('inf')

#defining epochs
epochs = 1

# empty lists to store training and validation loss of each epoch
train_losses=[]
valid_losses=[]

#for each epoch
for epoch in range(epochs):
     
    print('\n Epoch {:} / {:}'.format(epoch + 1, epochs))
    
    #train model
    train_loss, _ = train()
    
    #evaluate model
    valid_loss, _ = evaluate()
    
    #save the best model
    if valid_loss < best_valid_loss:
        best_valid_loss = valid_loss
        torch.save(model.state_dict(), 'saved_weights.pt')
    
    # append training and validation loss
    train_losses.append(train_loss)
    valid_losses.append(valid_loss)
    
    print(f'\nTraining Loss: {train_loss:.3f}')
    print(f'Validation Loss: {valid_loss:.3f}')

To load the best model weights from the saved file ‘saved_weights.pt’ using torch.load() and set them in the model using model.load_state_dict().

#load weights of best model
path = 'saved_weights.pt'
model.load_state_dict(torch.load(path))

Make Predictions

To make predictions on the test data using the trained model and converts the predictions to NumPy arrays. We compute classification metrics, including precision, recall, and F1-score, to evaluate the model’s performance using the classification_report function from scikit-learn’s metrics module.

# get predictions for test data
with torch.no_grad():
    preds = model(test_seq.to(device), test_mask.to(device))
    preds = preds.detach().cpu().numpy()

# model's performance
preds = np.argmax(preds, axis = 1)
print(classification_report(test_y, preds))

Conclusion

In conclusion, BERT is undoubtedly a breakthrough in using Machine Learning for Natural Language Processing. The fact that it’s approachable and allows fast fine-tuning will likely enable a wide range of practical applications in the future. This step-by-step BERT implementation tutorial empowers users to build powerful language models that can accurately understand and generate natural language.

Here are some critical points about BERT Implementation Guide:

• BERT’s success: BERT has revolutionized the field of natural language processing with its ability to capture deep contextualized representations, leading to remarkable performance improvements in various NLP tasks.
• Accessibility for everyone: This tutorial aims to make BERT implementation accessible to a wide range of users, regardless of their expertise level. By following the step-by-step guide, anyone can harness the power of BERT and build sophisticated language models.
• Real-world applications: BERT’s versatility empowers its application to real-world problems across industries, encompassing customer sentiment analysis, chatbots, recommendation systems, and more. Its implementation can drive tangible benefits and insights for businesses and researchers.

Frequently Asked Questions

kajal

Hi, I am Kajal Kumari. have completed my Master’s from IIT(ISM) Dhanbad in Computer Science & Engineering. As of now, I am working as Machine Learning Engineer in Hyderabad.
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