One breakthrough that has brought the natural processing of language to new levels over the past three years is the creation of transformers. No, I’m not talking about the huge robots that transform into vehicles in the well-known science-fiction film series directed by Michael Bay. Transformers are machine learning models that are semi-supervised and primarily used to process text data.
They have replaced neural networks with recurrent tasks that require natural language processing. This post aims to describe the workings of transformers and demonstrate how you can utilize them to enhance your project in machine learning.
How Transformers Work
Researchers first developed transformers from Google in the 2017 NIPS article “Attention is all You Need”. Transformers are made to operate on sequence data. They analyze an input sequence and create an output sequence, one element at one time.
A transformer, for instance, could be used to transform the sentences from English to French. In this scenario, sentences are described as a sequence of words. A transformer has two primary components: an encoder that operates primarily using the output sequence. The second one is a decoder that works on the output sequence of the target in the course of training and anticipates the next item of the sequence. When dealing with a machine translation issue, for instance, it could take a string comprising words in English and then iteratively predict the following French word according to the proper meaning until the phrase is fully translated.
Machine Learning Using Transformers
The use of machine learning (ML) has led to many innovative business models and innovations in fintech that have led to innovations in personal wealth management, automated fraud detection, and the ability to use real-time accounting software for small businesses. Since the beginning, one of the biggest problems with machine learning was the quality and quantity of data need to train machine learning models. However, recent advancements in Transformer structures have begun to change the situation.
Models based on transformers such as BERT (Bidirectional encoder representations of Transformers created at Google) along with GPT (Generative Pre-Training, made in the OpenAI lab) OpenAI) have led to the most significant advancements in machine learning over the recent years. These techniques were initially developed to process data from natural language. However, they have created thrilling new opportunities in various applications, including fintech.
One of the primary benefits of Transformer-based structures is that these models consider the context of text when studying it. Previous approaches to convert text into vectors such as Word2Vec could not differentiate from “bank” as a financial organization and “bank” as an edge of the river. However, models based on transformers can generate specific vectors for the context. The ability to distinguish between concepts based on context enhances the accuracy of the models’ predictions.
Transformer Models in Fintech Data Processing
Developing data-driven fintech products involves working with vast amounts of complex and often unstructured data. Natural language processing methods such as classification and named-entity recognition are essential to transform the unstructured or ambiguous transaction data into data that could be examined far more effectively. Once processed, this information can be utilized for a variety of applications.
For instance: Fraud detection classification of the transactions represented by transformer-based vectors to be “fraud” or “non-fraud.” Product suggestions by comparing Transformer-based vectors that represent products and their descriptions and measuring the similarities between these descriptions
Semantic Search: Retrieving the search results by using vector representations to compare the natural language search query by comparing vector representations to searchable data.
While these methods of processing languages have been in use for quite some time, Transformer models have made them more precise and efficient.
The transformer model applies the concept of learning transfer to natural language processing. Businesses like Facebook or Google create large models to comprehend a broader range of language processing. Many of these pre-trained models are accessible in open-source downloads and on platforms such as TensorFlow Hub or HuggingFace. These generic models that have been trained can be further refined for any specific domain. Since they already possess fundamental knowledge about the universe, considerably less training data is required to create a model rather than starting from scratch. This method provides superior
performance on tasks like the classification of entities or the extraction of entities. It also means that creating proof-of-concept for ML applications is much simpler and reduces the chance of failure.
Risks associated with Transformer Models in Fintech
Transformer models include a huge quantity of language-related knowledge. It comes with a price in terms of a vast amount of parameters. For instance, the largest version of BERT has 350 million parameters, whereas current GPT-3 models include one hundred billion variables. The enormous quantities of model data present two significant issues:
- The fine-tuning of these models for domain-specific reasons requires access to TPUs or GPUs. Many parameters also mean that it takes longer to calculate predictions. It can hinder the effectiveness of models based on Transformers in FinTech applications. Banks generally require high throughput, which could require expensive GPU-powered hardware, and sometimes operating all hours of the day.
- A third and often ignored issue is the inherent bias inherent in models that have been trained. Since these models are depends upon real-world texts (Wikipedia) and can be influenced by stereotypes portrayed within these documents. The exact collection used to pre-train large models is not always available, which means it’s not always clear what the source was if it contained personal information or an underlying bias. This topic has garnered plenty of interest lately, particularly in predictions that may result in financial and personal consequences.
What Comes Next
The mechanism behind Transformer models is develops in various ways, such as adaptability. While the first BERT model depends on two activities (mask prediction and prediction of the following sentence). The modern version of these models focuses on meta-learning, which is learning how to master. The current models, such as GPT-3, can even comprehend short instructions, such as changing natural language sentences in SQL commands and Python code.
However, models are growing dramatically, to the level where only companies such as OpenAI can host these models. GPT-3 is an example. It is available only via a hosting API. There is a chance that advanced meta-learning systems will be an actuality. However, they will most likely be sold as APIs at a price.
Transformer models are powerful tools to improve existing fintech solutions and open up new opportunities. However, they also pose new issues which require careful planning and the consideration of bias and hidden costs.