Introduction
Vectors form the bedrock of many intricate artificial intelligence applications, such as semantic search and anomaly detection. This article embarks on a journey to explore the fundamentals of embeddings, delving into sentence embeddings and vector representations. Practical approaches like mean pooling, cosine similarity, and the architecture of dual encoders with BERT will be discussed. Moreover, insights into training a dual encoder model, and using embeddings for anomaly detection and Vertex AI for fraud detection and content moderation will be provided.
Learning Objectives
There are several key learning goals for readers. First, it’s crucial to comprehend how vector embeddings represent words, sentences, and other data types in a continuous vector space. Understanding the tokenization process and the contribution of token embeddings to sentence embeddings is also essential. Additionally, grasping the key concepts and best practices for deploying embedding models in applications with Vertex AI to solve real – world AI challenges is a significant objective. Learning how to optimize and scale applications with Vertex AI through integrating embedding models for advanced analytics and intelligent decision – making is another important aspect. Gaining hands – on experience in training a dual encoder model by defining the encoder architecture and setting up the training process is a valuable takeaway. Finally, implementing anomaly detection using techniques like Isolation Forest to identify outliers based on embedding similarities is a key learning goal.
Understanding Vertex Embeddings
Vector embeddings are a general method for representing words or sentences in an appropriate space. The closeness of these embeddings is significant; the smaller the distance between two words in the vector space, the more similar they are. Initially used mainly in NLP, vector embeddings have now expanded to other domains like images, videos, audio, and graphs. For example, CLIP is a prominent model for multimodal learning, generating both image and text embeddings. Vector embeddings have diverse applications. LLMs use them as token embeddings after converting input tokens. They are also used in semantic searches to find the most relevant answers, in RAG for retrieving relevant chunks with sentence embeddings, and in recommendation systems to represent and find relevant products.
Understanding Sentence Embeddings
Sentence embeddings are generated by applying mathematical operations to token embeddings, which are produced by pre – trained models like BERT or GPT. For instance, with the BERT model, after word tokenization and computing word tokens, sentence embeddings can be generated using a mean pooling operation. The provided code example loads the bert – base – uncased model from Hugging Face and defines a function to compute sentence embeddings by applying mean pooling on token embeddings.
Cosine Similarity of Sentence Embeddings
Cosine similarity is a widely adopted metric for measuring the similarity between two vectors, making it suitable for comparing sentence embeddings. The provided code includes functions to compute the cosine similarity matrix and plot the similarity heatmap. By analyzing sentences from different topics, the heatmap shows the similarity between them, though the initial results may not always accurately reflect the actual content.
How to Train the Dual Encoder?
A dual encoder architecture employs two independent BERT encoders, one for questions and one for answers. Each input sequence passes through its respective encoder layers, and the model extracts the [CLS] token embedding as a compact representation. The cosine similarity of the [CLS] token embeddings for questions and answers is calculated, and this score is used in the loss function during training. The [CLS] token is important as it pools information from all other tokens in the sequence, leveraging the self – attention mechanism in BERT.
Dual Encoder for Question – Answer Tasks
Dual encoders are commonly used in question – answer tasks to compute the relevance between questions and potential answers. The provided code defines an Encoder class, which can be used for training like any deep – learning model to encode questions and answers into a shared embedding space.
Training the Dual Encoder
Training the dual encoder involves several steps. First, hyperparameters such as embedding size, sequence length, and batch size need to be defined. Then, the tokenizer, question encoder, and answer encoder are initialized. After that, the dataloader, optimizer, and loss function are set up. The model is then trained for a specified number of epochs and batch size while minimizing the loss. Once trained, the encoder models can be used to generate embeddings and evaluate relevance.
Application of Embeddings using Vertex AI
Vertex AI can be used to apply embeddings in various industrial applications such as anomaly detection, fraud detection, content moderation, and search and recommendation systems. The process includes creating a dataset from Stack Overflow using BigQuery, generating text embeddings in batches, getting embeddings for a batch of data, and identifying anomalies using algorithms like Isolation Forest.
Conclusion
Vector embeddings are integral to modern machine – learning applications, enabling efficient semantic information representation and retrieval. By using pre – trained models, dual encoders, and anomaly detection techniques, we can enhance the accuracy and efficiency of various tasks. Understanding these concepts and their implementation, especially with tools like Vertex AI, provides a solid foundation for addressing real – world NLP and broader AI challenges.
