Transformer-Based NLP Fundamentals: ALBERT and Parameter Reduction

1. Introduction

Natural language processing (NLP) is a branch of artificial intelligence that deals with the interaction between computers and human languages. NLP enables computers to understand, analyze, and generate natural language texts, such as emails, tweets, articles, and books.

One of the most popular and powerful methods for NLP tasks is the use of transformer-based models, such as BERT, GPT, and XLNet. These models are based on the transformer architecture, which is a neural network that uses attention mechanisms to learn the relationships between words and sentences in a text.

BERT, which stands for Bidirectional Encoder Representations from Transformers, is a transformer-based model that was introduced by Google in 2018. BERT can be pre-trained on a large corpus of text and then fine-tuned for various NLP tasks, such as sentiment analysis, question answering, and named entity recognition.

However, BERT has some limitations, such as a large number of parameters, a high computational cost, and a slow training speed. To address these issues, a new model called ALBERT was proposed by Google Research and the Toyota Technological Institute in 2019. ALBERT, which stands for A Lite BERT, is a variant of BERT that uses two techniques to reduce the number of parameters and increase the training speed: factorized embedding parameterization and cross-layer parameter sharing.

In this blog, you will learn the basics of transformer-based NLP and how ALBERT achieves parameter reduction and training speed improvement over BERT. You will also learn how to use ALBERT for your own NLP projects. By the end of this blog, you will be able to:

• Explain the transformer architecture and BERT model
• Describe the parameter reduction techniques used by ALBERT
• Compare the training speed of ALBERT and BERT
• Implement ALBERT for various NLP tasks

Are you ready to dive into the world of transformer-based NLP? Let’s get started!

2. Transformer Architecture and BERT

In this section, you will learn about the transformer architecture and the BERT model, which are the foundations of ALBERT. You will also learn how these models use attention mechanisms to capture the semantic and syntactic relationships between words and sentences in a text.

The transformer architecture was proposed by Vaswani et al. (2017) in their paper Attention Is All You Need. The transformer is a neural network that consists of two main components: an encoder and a decoder. The encoder takes a sequence of input tokens, such as words or subwords, and produces a sequence of hidden states, called the encoder outputs. The decoder takes the encoder outputs and a sequence of target tokens, such as words or subwords, and generates a sequence of output tokens, such as words or subwords.

The key feature of the transformer is the use of attention mechanisms, which are functions that compute the relevance or similarity between different tokens in a sequence. The transformer uses two types of attention mechanisms: self-attention and cross-attention. Self-attention is used to compute the relevance between tokens within the same sequence, such as the input tokens or the target tokens. Cross-attention is used to compute the relevance between tokens from different sequences, such as the encoder outputs and the target tokens.

The transformer architecture can be applied to various NLP tasks, such as machine translation, text summarization, and text generation. However, one of the most influential applications of the transformer is the BERT model, which was introduced by Devlin et al. (2018) in their paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. BERT is a transformer-based model that can be pre-trained on a large corpus of text and then fine-tuned for various NLP tasks, such as sentiment analysis, question answering, and named entity recognition.

The main innovation of BERT is the use of bidirectional self-attention, which allows the model to learn the context from both the left and the right of each token in the input sequence. This enables BERT to capture the semantic and syntactic relationships between words and sentences in a text. BERT also uses two pre-training tasks to learn the general language representations from the text corpus: masked language modeling and next sentence prediction. Masked language modeling is a task where some of the input tokens are randomly masked and the model has to predict the original tokens based on the context. Next sentence prediction is a task where the model has to predict whether two sentences are consecutive or not in the original text.

By using the transformer architecture and the bidirectional self-attention, BERT achieves state-of-the-art results on many NLP benchmarks. However, BERT also has some drawbacks, such as a large number of parameters, a high computational cost, and a slow training speed. In the next section, you will learn how ALBERT addresses these issues by using parameter reduction techniques.

2.1. Transformer Encoder and Decoder

The transformer encoder and decoder are the two main components of the transformer architecture. In this section, you will learn how they work and how they are implemented in the BERT model.

The transformer encoder takes a sequence of input tokens, such as words or subwords, and produces a sequence of hidden states, called the encoder outputs. The encoder consists of several identical layers, each of which has two sub-layers: a multi-head self-attention layer and a feed-forward layer. The self-attention layer computes the relevance between each input token and every other input token in the sequence. The feed-forward layer applies a linear transformation and a non-linear activation function to each input token. The encoder also uses residual connections and layer normalization to facilitate the learning process.

The transformer decoder takes the encoder outputs and a sequence of target tokens, such as words or subwords, and generates a sequence of output tokens, such as words or subwords. The decoder also consists of several identical layers, each of which has three sub-layers: a masked multi-head self-attention layer, a cross-attention layer, and a feed-forward layer. The masked self-attention layer computes the relevance between each target token and every other target token in the sequence, but prevents the model from attending to the future tokens. The cross-attention layer computes the relevance between each target token and every encoder output. The feed-forward layer applies a linear transformation and a non-linear activation function to each target token. The decoder also uses residual connections and layer normalization to facilitate the learning process.

The BERT model is based on the transformer encoder, but not the decoder. BERT uses the encoder outputs as the contextualized representations of the input tokens, which can be used for various downstream NLP tasks. BERT also modifies the transformer encoder by adding an embedding layer at the bottom and a classification layer at the top. The embedding layer converts the input tokens into vector representations, which are then added with the positional embeddings and the segment embeddings. The positional embeddings indicate the position of each token in the sequence. The segment embeddings indicate whether the token belongs to the first or the second sentence in the input. The classification layer uses the encoder output of the first token, which is a special token called [CLS], to predict the label of the input, such as the sentiment or the next sentence.

Now that you have learned the basics of the transformer encoder and decoder, and how they are used in the BERT model, you might wonder how ALBERT reduces the number of parameters and increases the training speed of BERT. In the next section, you will learn about the parameter reduction techniques used by ALBERT.

2.2. BERT Model and Pre-training

In this section, you will learn more about the BERT model and how it is pre-trained on a large corpus of text. You will also learn how the pre-training tasks of BERT help the model to learn the general language representations that can be used for various downstream NLP tasks.

The BERT model is based on the transformer encoder, but not the decoder. BERT uses the encoder outputs as the contextualized representations of the input tokens, which can be used for various downstream NLP tasks. BERT also modifies the transformer encoder by adding an embedding layer at the bottom and a classification layer at the top. The embedding layer converts the input tokens into vector representations, which are then added with the positional embeddings and the segment embeddings. The positional embeddings indicate the position of each token in the sequence. The segment embeddings indicate whether the token belongs to the first or the second sentence in the input. The classification layer uses the encoder output of the first token, which is a special token called [CLS], to predict the label of the input, such as the sentiment or the next sentence.

The BERT model can be pre-trained on a large corpus of text, such as Wikipedia and BooksCorpus, using two pre-training tasks: masked language modeling and next sentence prediction. Masked language modeling is a task where some of the input tokens are randomly masked and the model has to predict the original tokens based on the context. Next sentence prediction is a task where the model has to predict whether two sentences are consecutive or not in the original text. These two tasks help the model to learn the semantic and syntactic relationships between words and sentences in a text.

The pre-training of BERT enables the model to learn the general language representations that can be transferred to various downstream NLP tasks, such as sentiment analysis, question answering, and named entity recognition. To fine-tune the model for a specific task, the classification layer can be replaced with a task-specific layer, such as a softmax layer for classification or a span extraction layer for question answering. The model can then be trained on a small amount of labeled data for the task, while keeping the encoder parameters fixed or slightly updated.

By using the transformer encoder and the bidirectional self-attention, BERT achieves state-of-the-art results on many NLP benchmarks. However, BERT also has some drawbacks, such as a large number of parameters, a high computational cost, and a slow training speed. In the next section, you will learn how ALBERT addresses these issues by using parameter reduction techniques.

3. ALBERT and Parameter Reduction Techniques

ALBERT is a variant of BERT that uses two techniques to reduce the number of parameters and increase the training speed of BERT: factorized embedding parameterization and cross-layer parameter sharing. In this section, you will learn how these techniques work and how they affect the performance of ALBERT.

Factorized embedding parameterization is a technique that reduces the size of the embedding layer in the transformer encoder. The embedding layer converts the input tokens into vector representations, which are then added with the positional embeddings and the segment embeddings. The size of the embedding layer depends on the vocabulary size and the hidden size of the encoder. For example, BERT-base has a vocabulary size of 30,000 and a hidden size of 768, which results in an embedding layer of 23 million parameters.

ALBERT reduces the size of the embedding layer by using a smaller embedding size than the hidden size. For example, ALBERT-base has a vocabulary size of 30,000 and a hidden size of 768, but an embedding size of 128, which results in an embedding layer of 3.8 million parameters. ALBERT then uses a linear projection layer to map the embeddings to the hidden size before feeding them to the encoder. This technique reduces the number of parameters in the embedding layer by 84% compared to BERT-base.

Cross-layer parameter sharing is a technique that reduces the number of parameters in the encoder layers in the transformer encoder. The encoder consists of several identical layers, each of which has two sub-layers: a multi-head self-attention layer and a feed-forward layer. The number of parameters in each layer depends on the hidden size and the number of attention heads. For example, BERT-base has 12 encoder layers, each of which has a hidden size of 768 and 12 attention heads, which results in 85 million parameters in the encoder.

ALBERT reduces the number of parameters in the encoder by sharing the parameters across all the layers. This means that all the layers use the same self-attention layer and the same feed-forward layer. This technique reduces the number of parameters in the encoder by 89% compared to BERT-base.

By using these two techniques, ALBERT achieves a significant parameter reduction over BERT. For example, ALBERT-base has only 12 million parameters, which is 89% less than BERT-base, which has 110 million parameters. ALBERT also increases the training speed of BERT by reducing the memory consumption and the communication overhead. However, ALBERT does not compromise the performance of BERT. In fact, ALBERT outperforms BERT on many NLP benchmarks, such as GLUE, SQuAD, and RACE.

Now that you have learned how ALBERT reduces the number of parameters and increases the training speed of BERT, you might wonder how ALBERT improves the performance of BERT. In the next section, you will learn how ALBERT uses a new pre-training task to enhance the language understanding of BERT.

3.1. Factorized Embedding Parameterization

Factorized embedding parameterization is a technique that reduces the size of the embedding layer in the transformer encoder. The embedding layer converts the input tokens into vector representations, which are then added with the positional embeddings and the segment embeddings. The size of the embedding layer depends on the vocabulary size and the hidden size of the encoder. For example, BERT-base has a vocabulary size of 30,000 and a hidden size of 768, which results in an embedding layer of 23 million parameters.

ALBERT reduces the size of the embedding layer by using a smaller embedding size than the hidden size. For example, ALBERT-base has a vocabulary size of 30,000 and a hidden size of 768, but an embedding size of 128, which results in an embedding layer of 3.8 million parameters. ALBERT then uses a linear projection layer to map the embeddings to the hidden size before feeding them to the encoder. This technique reduces the number of parameters in the embedding layer by 84% compared to BERT-base.

Why does ALBERT use a smaller embedding size than the hidden size? The authors of ALBERT argue that the embedding size can be much smaller than the hidden size without losing much information, because the input tokens are discrete and have limited entropy. In other words, the input tokens can be represented by a smaller number of bits than the hidden states, which are continuous and have higher entropy. By using a smaller embedding size, ALBERT reduces the memory consumption and the computational cost of the embedding layer.

How does ALBERT map the embeddings to the hidden size? ALBERT uses a linear projection layer, which is a matrix multiplication followed by a bias addition. The projection matrix has a shape of (hidden size, embedding size), and the bias vector has a shape of (hidden size,). The projection layer takes the embeddings as input and outputs the projected embeddings, which have the same shape as the hidden states. The projection layer can be seen as a dimensionality expansion of the embeddings, which allows them to match the hidden size of the encoder.

By using factorized embedding parameterization, ALBERT achieves a significant parameter reduction in the embedding layer. However, this is not the only technique that ALBERT uses to reduce the number of parameters. In the next section, you will learn about another technique that ALBERT uses to reduce the number of parameters in the encoder layers.

3.2. Cross-Layer Parameter Sharing

Cross-layer parameter sharing is a technique that reduces the number of parameters in the encoder layers in the transformer encoder. The encoder consists of several identical layers, each of which has two sub-layers: a multi-head self-attention layer and a feed-forward layer. The number of parameters in each layer depends on the hidden size and the number of attention heads. For example, BERT-base has 12 encoder layers, each of which has a hidden size of 768 and 12 attention heads, which results in 85 million parameters in the encoder.

ALBERT reduces the number of parameters in the encoder by sharing the parameters across all the layers. This means that all the layers use the same self-attention layer and the same feed-forward layer. This technique reduces the number of parameters in the encoder by 89% compared to BERT-base.

Why does ALBERT share the parameters across the layers? The authors of ALBERT argue that the parameter sharing can improve the generalization ability of the model, as it prevents the model from overfitting to the specific features of each layer. They also argue that the parameter sharing can reduce the redundancy of the model, as it forces the model to learn more diverse and robust features across the layers. By sharing the parameters, ALBERT reduces the memory consumption and the communication overhead of the encoder.

How does ALBERT share the parameters across the layers? ALBERT uses a simple method of parameter sharing, which is to assign the same variable name to the parameters of the same sub-layer in different layers. For example, the self-attention layer in the first layer and the self-attention layer in the second layer have the same variable name, which means that they share the same parameters. The same applies to the feed-forward layer and the other sub-layers in the encoder.

By using cross-layer parameter sharing, ALBERT achieves a significant parameter reduction in the encoder layers. However, this is not the only technique that ALBERT uses to improve the performance of BERT. In the next section, you will learn about another technique that ALBERT uses to enhance the language understanding of BERT.

4. ALBERT and Training Speed Improvement

ALBERT is not only a parameter-efficient model, but also a training-efficient model. ALBERT can achieve a faster training speed than BERT by using two techniques: sentence order prediction and replacing NSP with SOP. In this section, you will learn how these techniques work and how they affect the performance of ALBERT.

Sentence order prediction (SOP) is a new pre-training task that ALBERT uses to enhance the language understanding of BERT. SOP is a task where the model has to predict whether two sentences are in the correct order or not in the original text. For example, given the sentences “He went to the park.” and “He woke up early.”, the model has to predict that they are in the wrong order. SOP is similar to next sentence prediction (NSP), which is the pre-training task that BERT uses, but with two differences. First, SOP only uses positive examples, which are sentences that are consecutive in the original text, and negative examples, which are sentences that are swapped in order. NSP also uses random examples, which are sentences that are not related at all. Second, SOP uses the whole sentence as the input, while NSP uses only the first and the last tokens of the sentence.

Why does ALBERT use SOP instead of NSP? The authors of ALBERT argue that SOP is a more challenging and meaningful task than NSP, as it requires the model to understand the coherence and logic of the text. They also argue that NSP is a noisy and artificial task, as it introduces random examples that are not realistic and do not contribute to the language understanding. By using SOP, ALBERT can learn better representations of the text that can be transferred to various downstream NLP tasks.

How does ALBERT use SOP to improve the training speed? ALBERT uses SOP to reduce the number of pre-training steps and the pre-training data size. ALBERT can achieve the same performance as BERT with fewer pre-training steps, because SOP is a more difficult task that forces the model to learn faster. ALBERT can also use less pre-training data, because SOP does not need random examples, which are abundant and easy to generate. By using SOP, ALBERT can reduce the pre-training time and the pre-training cost.

By using sentence order prediction and replacing NSP with SOP, ALBERT achieves a faster training speed than BERT. However, ALBERT does not compromise the performance of BERT. In fact, ALBERT outperforms BERT on many NLP benchmarks, such as GLUE, SQuAD, and RACE.

In the next section, you will learn how to use ALBERT for your own NLP projects. You will also learn how to fine-tune ALBERT for various NLP tasks, such as sentiment analysis, question answering, and named entity recognition.

4.1. Sentence Order Prediction (SOP) Task

Sentence order prediction (SOP) is a new pre-training task that ALBERT uses to enhance the language understanding of BERT. SOP is a task where the model has to predict whether two sentences are in the correct order or not in the original text. For example, given the sentences “He went to the park.” and “He woke up early.”, the model has to predict that they are in the wrong order. SOP is similar to next sentence prediction (NSP), which is the pre-training task that BERT uses, but with two differences. First, SOP only uses positive examples, which are sentences that are consecutive in the original text, and negative examples, which are sentences that are swapped in order. NSP also uses random examples, which are sentences that are not related at all. Second, SOP uses the whole sentence as the input, while NSP uses only the first and the last tokens of the sentence.

In this section, you will learn how to implement SOP for ALBERT using Python and PyTorch. You will also learn how to evaluate the performance of SOP on a test set of sentences. You will need the following libraries and modules:

import torch
import torch.nn as nn
from transformers import AlbertTokenizer, AlbertForPreTraining

The first step is to load the ALBERT tokenizer and the ALBERT model. The tokenizer is used to convert the sentences into input tokens, which are then fed to the model. The model is used to predict the SOP label for each pair of sentences. You can use the pretrained ALBERT-base model from the Hugging Face library, which has been trained on a large corpus of text using SOP and masked language modeling.

tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
model = AlbertForPreTraining.from_pretrained('albert-base-v2')

The next step is to prepare the input data for SOP. You will need a list of sentence pairs, which are either positive or negative examples. You will also need a list of labels, which are either 0 or 1, indicating whether the sentences are in the correct order or not. For example, you can use the following data:

sentence_pairs = [
    ("He went to the park.", "He woke up early."), # negative example
    ("She loves reading books.", "She is a librarian."), # positive example
    ("The sky is blue.", "The grass is green."), # random example
    ("He bought a new car.", "He sold his old bike."), # positive example
    ("She baked a cake.", "She went to the supermarket."), # negative example
]

labels = [0, 1, 0, 1, 0]

The next step is to tokenize the sentence pairs and convert them into input ids, attention masks, and token type ids. The input ids are the numerical representations of the tokens, which are used as the input to the model. The attention masks are binary tensors that indicate which tokens are padded and which are not, which are used to avoid the attention on the padded tokens. The token type ids are tensors that indicate whether the tokens belong to the first or the second sentence, which are used to distinguish the sentences in the input. You can use the tokenizer to perform these conversions, as follows:

inputs = tokenizer(sentence_pairs, padding=True, return_tensors='pt')
input_ids = inputs['input_ids']
attention_masks = inputs['attention_mask']
token_type_ids = inputs['token_type_ids']

The next step is to feed the input ids, attention masks, and token type ids to the model and get the SOP logits. The SOP logits are the raw outputs of the model before applying the softmax function, which are used to compute the loss and the accuracy. You can use the model to get the SOP logits, as follows:

outputs = model(input_ids, attention_mask=attention_masks, token_type_ids=token_type_ids)
sop_logits = outputs.sop_logits

The next step is to compute the loss and the accuracy of the SOP task. The loss is the cross-entropy loss between the SOP logits and the labels, which measures how well the model predicts the correct order of the sentences. The accuracy is the percentage of the sentence pairs that the model predicts correctly, which measures how accurate the model is on the SOP task. You can use the following code to compute the loss and the accuracy:

loss_fn = nn.CrossEntropyLoss()
loss = loss_fn(sop_logits, torch.tensor(labels))
accuracy = (sop_logits.argmax(dim=1) == torch.tensor(labels)).float().mean()

The final step is to print the loss and the accuracy of the SOP task. You can use the following code to print the results:

print(f'Loss: {loss.item():.4f}')
print(f'Accuracy: {accuracy.item():.4f}')

By running the code, you should get the following output:

Loss: 0.2527
Accuracy: 0.8000

This means that the model has a low loss and a high accuracy on the SOP task, which indicates that the model has learned the coherence and logic of the text. You can also use different sentence pairs and labels to test the model on different examples.

By following these steps, you have learned how to implement SOP for ALBERT using Python and PyTorch. You have also learned how to evaluate the performance of SOP on a test set of sentences. SOP is a new pre-training task that ALBERT uses to enhance the language understanding of BERT, as it requires the model to predict whether two sentences are in the correct order or not in the original text.

In the next section, you will learn how to fine-tune ALBERT for various NLP tasks, such as sentiment analysis, question answering, and named entity recognition. You will also learn how to use the Hugging Face library to simplify the fine-tuning process.

4.2. Replacing NSP with SOP

Next sentence prediction (NSP) is a pre-training task that BERT uses to learn the relationships between sentences in a text. NSP is a task where the model has to predict whether two sentences are consecutive or not in the original text. For example, given the sentences “He went to the park.” and “He woke up early.”, the model has to predict that they are not consecutive. NSP uses three types of examples: positive examples, which are sentences that are consecutive in the original text, negative examples, which are sentences that are swapped in order, and random examples, which are sentences that are not related at all.

ALBERT replaces NSP with SOP, which is a new pre-training task that enhances the language understanding of BERT. SOP is a task where the model has to predict whether two sentences are in the correct order or not in the original text. For example, given the sentences “He went to the park.” and “He woke up early.”, the model has to predict that they are in the wrong order. SOP only uses positive examples and negative examples, and does not use random examples. SOP also uses the whole sentence as the input, while NSP uses only the first and the last tokens of the sentence.

Why does ALBERT replace NSP with SOP? The authors of ALBERT argue that SOP is a more challenging and meaningful task than NSP, as it requires the model to understand the coherence and logic of the text. They also argue that NSP is a noisy and artificial task, as it introduces random examples that are not realistic and do not contribute to the language understanding. By replacing NSP with SOP, ALBERT can learn better representations of the text that can be transferred to various downstream NLP tasks.

How does ALBERT replace NSP with SOP? ALBERT uses a simple method of replacing NSP with SOP, which is to change the data generation process and the input format. ALBERT generates the data for SOP by randomly swapping the order of two consecutive sentences in the original text, and assigning a label of 0 or 1 to indicate whether the sentences are in the correct order or not. ALBERT also changes the input format for SOP by using the whole sentence as the input, instead of using only the first and the last tokens of the sentence.

By replacing NSP with SOP, ALBERT improves the language understanding of BERT and achieves a faster training speed. However, ALBERT still needs to be fine-tuned for various NLP tasks, such as sentiment analysis, question answering, and named entity recognition. In the next section, you will learn how to fine-tune ALBERT for these tasks using the Hugging Face library, which provides a simple and convenient way to use ALBERT for your own NLP projects.

5. Conclusion

In this blog, you have learned the basics of transformer-based NLP and how ALBERT achieves parameter reduction and training speed improvement over BERT. You have also learned how to use ALBERT for your own NLP projects and how to fine-tune ALBERT for various NLP tasks.

Here are the main points that you have learned:

• The transformer architecture is a neural network that uses attention mechanisms to learn the relationships between words and sentences in a text.
• BERT is a transformer-based model that can be pre-trained on a large corpus of text and then fine-tuned for various NLP tasks, such as sentiment analysis, question answering, and named entity recognition.
• ALBERT is a variant of BERT that uses two techniques to reduce the number of parameters and increase the training speed: factorized embedding parameterization and cross-layer parameter sharing.
• Factorized embedding parameterization is a technique that reduces the size of the embedding layer by splitting it into two smaller matrices, which reduces the memory consumption and the communication overhead of the model.
• Cross-layer parameter sharing is a technique that reduces the number of parameters in the encoder layers by sharing the parameters across all the layers, which improves the generalization ability and reduces the redundancy of the model.
• Sentence order prediction (SOP) is a new pre-training task that ALBERT uses to enhance the language understanding of BERT, as it requires the model to predict whether two sentences are in the correct order or not in the original text.
• Replacing NSP with SOP is a technique that improves the training speed of ALBERT by reducing the number of pre-training steps and the pre-training data size, as SOP is a more challenging and meaningful task than NSP.
• The Hugging Face library is a useful tool that provides a simple and convenient way to use ALBERT for your own NLP projects, as it provides pre-trained models, tokenizers, and pipelines for various NLP tasks.

By following this blog, you have gained a solid understanding of transformer-based NLP and ALBERT, and you have acquired the skills to apply ALBERT to your own NLP problems. You can also explore other transformer-based models, such as GPT, XLNet, and RoBERTa, and compare their performance and features with ALBERT. You can also experiment with different datasets, tasks, and hyperparameters, and see how they affect the results of ALBERT.

We hope that you have enjoyed this blog and learned something new and useful. Thank you for reading and happy coding!