Understanding Transformers and How To Use BERT

     The introduction of the transformer architecture in the 2017 paper "Attention Is All You Need" by Vaswani et al. brought transformative changes to natural language processing (NLP). Transformers have become a cornerstone in many cutting-edge machine learning models, including large language models like ChatGPT, BERT, and LLAMA. These models have demonstrated remarkable performance in various NLP tasks, marking a significant advancement in the field. In this blog post, we'll explore the core concepts behind transformers and learn how to use them. The tutorial covers:

  1. Introduction to transformers
  2. Key Components of Transformers
  3. Practical Usage of Transformers
  4. Conclusion

     Let's get started.


Introduction to transformers

    Transformers emerged to address challenges faced by traditional sequence-to-sequence models in handling long-range dependencies. Their self-attention mechanisms revolutionized capturing contextual information essential for language understanding.


    The transformer architecture employs self-attention to weigh the significance of input sequence elements, enabling efficient capture of intricate relationships. The self-attention mechanism allows for the simultaneous processing of all words, effectively capturing contextual dependencies.

Self-attention mechanism

    The self-attention mechanism lies at the heart of transformers, enabling them to capture intricate dependencies within a sequence of words. Imagine you have a sentence: "The cat sat on the mat." With traditional models, each word would be processed one at a time, neglecting the relationships between distant words. Self-attention, however, empowers each word to consider every other word in the sequence when encoding information. Here's how it works: 

  • Compute Query, Key, and Value:  For each word, calculate query, key, and value vectors. In self-attention, each token in the input sequence generates three vectors: query, key, and value.
    • The query represents the token or word being analyzed. 
    • The key vector represents the token's context or the information it holds. 
    • The value vector contains the actual information associated with the token. 
  • Attention Scores: Calculate the attention scores by taking the dot product of the query of one word with the keys of all other words.
  • Softmax and Weighted Sum: Apply a softmax function to obtain attention weights, determining how much each word contributes to the current word's representation. The weighted sum of these contributions becomes the new representation. 
  • Capturing Long-Range Dependencies Unlike sequential models, self-attention allows any word to influence the representation of any other word, effectively capturing long-range dependencies. This parallel processing significantly enhances the model's ability to understand relationships within the sequence.

Multi-head attention

    To enhance the capacity to learn from different aspects of the input sequence, transformers use multi-head attention. In essence, this involves running the self-attention mechanism multiple times in parallel, each with a different set of learned parameters. The outputs from these multiple attention heads are then concatenated and linearly transformed, providing the model with a richer understanding of the input sequence.

Key Components of Transformers

    Understanding the transformer architecture's key components is vital to comprehend its functionality. Let's explore these components:

  • Embedding Layer: Initiates the process by transforming input tokens into vectors, assigning unique vectors to capture semantic meanings.
  • Encoder-Decoder Architecture: Originally for sequence-to-sequence tasks, transformers prove versatile. The encoder processes input, while the decoder generates the output sequence.
  • Encoder Layers: Multiple identical layers with sub-layers, including multi-head self-attention and a feed-forward network, maintain stability and efficiency through residual connections and layer normalization.
  • Decoder Layers: Similar to the encoder but with an added cross-attention layer, enabling focus on different parts of the input sequence for generating contextually rich output sequences.


Practical Usage of Transformers

    In this section, we will learn how to set up the environment, load a pre-trained transformer model, tokenize input text, and perform inference. Here, we cover initial steps for using transformers in practical applications such as text classification, sentiment analysis, and more. 

1. Installing the Library:

    To begin our journey with transformers, we first need to install the Hugging Face transformers library. Open your terminal or command prompt and run the following command:

pip install transformers

    This command will download and install the necessary dependencies, making the transformers library available for use in your Python environment.

2. Loading Pre-trained Models:

    After installing the library, we load a pre-trained BERT model.

    BERT stands for Bidirectional Encoder Representations from Transformers. It's a transformer-based machine learning model designed for natural language processing (NLP) tasks. Unlike previous models, BERT's uniqueness lies in its bidirectional pre-training approach. By considering both left and right contexts in all layers, BERT comprehends word meanings better, enhancing performance across NLP tasks like text classification, named entity recognition, and question answering.

    In your Python script or Jupyter notebook, you can accomplish this with just a few lines of code:

from transformers import BertModel, BertTokenizer

# Load pre-trained BERT model and tokenizer
model = BertModel.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

Here, we're loading a pre-trained BERT model ('bert-base-uncased') and its corresponding tokenizer. This prepares the model for tokenizing and processing text.

3. Tokenization:

    Tokenization is a crucial step in preparing text for input into a transformer model. Let's see how to tokenize a sentence:

# Tokenize input text
input_text = "Transformers are amazing!"
tokens = tokenizer(input_text, return_tensors='pt')

    In this example, we use the tokenizer to convert the input text into a format that the model can understand. The return_tensors='pt' argument specifies that the output should be in PyTorch tensor format.

4. Inference and Prediction:

Now, let's perform inference and make predictions with our pre-trained transformer model:

# Forward pass through the model
outputs = model(**tokens)

# Get the model's output (logits)
logits = outputs.last_hidden_state

# Perform any additional processing or extract relevant information as needed

    This code snippet demonstrates how to obtain the model's output after tokenization. Depending on the specific task (classification, regression, etc.), you might need to further process the logits or extract relevant information for your use case.


    In this tutorial, we explored the realm of transformers and uncovered the fundamental steps to harness their power in Natural Language Processing (NLP). As we delved into the intricacies of installation, model loading, tokenization, and inference, we witnessed the transformative capabilities that make transformers indispensable in the field of NLP.

    Transformers, with their ability to capture intricate language patterns and relationships, emerge as a formidable tool, unlocking new possibilities in understanding and processing textual data.  In our upcoming tutorials, we'll delve into more practical cases, unveiling the versatility of transformers in solving real-world NLP challenges. Stay tuned for more insights and hands-on experiences with transformers!
  1. Attention is All You Need, https://arxiv.org/abs/1706.03762
  2. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding https://arxiv.org/abs/1810.04805
  3. BERT https://huggingface.co/docs/transformers/model_doc/bert

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