Fundamentos de Transformers y su Relevancia en NLP

The Transformers revolution in natural language processing has completely changed the paradigm of how machines understand and process text. This innovative architecture, shared by models such as GPT, BERT, RoBERTa and ALBERT, has enabled significant advances in contextual language understanding. Unlike traditional recurrent neural networks, Transformers can analyze entire sentences in parallel, capturing long-range relationships while maintaining the overall context of the text.

What is the origin of Transformers?

Transformers were born from the paper entitled "Attention is All You Need", published by Google. This revolutionary work broke the paradigm of recurrent neural networks that analyzed the text word by word, introducing an approach that allows:

• Process sequences in parallel, significantly increasing efficiency.
• Capture long-range relationships between distant words in a text.
• Maintain contextual memory of the entire sentence, not just individual words.

The key concept introduced by Transformers is the self-attention mechanism, which allows each word in a sequence to "pay attention" to all other words, determining their contextual relevance.

Transformer-based architectures

There are several popular architectures based on Transformers, each with specific characteristics:

• BERT(Bidirectional Encoder Representations from Transformers): uses only the encoder part of the Transformer architecture. It has been trained with large amounts of text and has a lot of context. For English, a popular version has 12 layers.

• RoBERTa: A variant of BERT with modifications in its training.

• ALBERT: Another variant that modifies aspects of the training and structure.

• DistilBERT: A lighter and faster version of BERT, with only 6 layers instead of 12, ideal for hardware with limited resources such as smaller CPUs or GPUs.

The choice of model will depend on the hardware available, the task to be solved and the computational resources available.

How to explore the configuration of these models?

To further explore the configuration of these architectures, we can use the Hugging Face Transformers library, which allows us to interact with these advanced models.

# Installation (if not already installed)pip install transformers
 # Import the necessary librariesfrom transformers import BertConfig, BertModelimport torch

It is important to note that the use of GPU is recommended due to the high computational cost of these models. Google Colab already has the Transformers library installed by default.

Exploring the BERT configuration

We can examine the configuration of a BERT model for Spanish:

# load model configurationconfig = BertConfig.from_pretrained("dccuchile/bert-base-spanish-wwm-cased")print(config).

When executing this code, we will see that the model has 12 hidden layers. These layers are the ones that process the input text and generate the final representation. The higher the number of layers, the higher the complexity and potentially better results, but also the higher the computational cost.

Visualizing the architecture

We can also visualize the complete architecture of the model:

# load model model = BertModel.from_pretrained("dccuchile/bert-base-spanish-wwm-cased")print(model).

By executing this code, we can observe:

• The embeddings layer that converts text into numeric representations.
• Theattention layers, which are the heart of the Transformers
• Intermediate and output layers
• Mechanisms such as dropout to avoid overfitting

How does the self-attention mechanism work?

The self-attention mechanism is the fundamental component that distinguishes Transformers. Let's see how it works conceptually:

1. For each token (word or subword) in a sequence, three vectors are computed:

   • Query (Q): What the token is "asking".
   • Key (K): What other tokens "offer".
   • Value (V): The information contained in the token.

2. A matrix multiplication is performed between these vectors to obtain the attention weights.

3. For each token, an attention score is calculated with respect to all other tokens.

4. A softmax function is applied to these scores to normalize them.

5. Finally, a contextual representation is generated for each token based on these attention weights.

This process allows each token to take into account the full context of the sequence, resulting in much richer and more accurate representations of the language.

# Example of tokenization and obtaining hidden statesfrom transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-cased")text = "The Samsung Galaxy S21 product arrived on March 12 and exceeded my expectations."
 # Tokenize textinputs = tokenizer(text,  return_tensors="pt")outputs = model(**inputs, output_hidden_states=True)
 # Get hidden stateshidden_states = outputs.hidden_statesprint(f "Number of layers: {len(hidden_states)}") # 13 (12 layers + embedding).

Transformers have revolutionized the field of natural language processing, enabling significant advances in tasks such as text classification, entity detection, machine translation and text generation. Although this introduction may seem theoretical, it lays the groundwork for understanding how to implement these powerful tools in practical applications. Have you worked with any of these models? Share your experience and the applications you have developed using Transformers.