The world of Natural Language Processing (NLP) has witnessed a remarkable evolution, particularly in the realm of search and information retrieval. Two prominent architectures that have significantly impacted this field are BERT (Bidirectional Encoder Representations from Transformers) and Sentence Transformers. While both are based on the Transformer architecture, they are tailored for different tasks and exhibit distinct characteristics that influence their performance in search applications. Understanding these differences is crucial for selecting the most appropriate model for a given search scenario, optimizing for speed, accuracy, and relevance. The purpose of this article is to delve into these differences, exploring the architectures, training methodologies, and performance implications of each model in the context of search. By examining these aspects, we can gain valuable insights into when and why Sentence Transformers typically outperform standard BERT in search-related tasks.
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BERT, introduced by Google in 2018, revolutionized NLP by enabling bidirectional understanding of context in text. This means BERT processes both preceding and following words to understand the meaning of a word in a sentence. The original BERT model was pretrained on a massive corpus of text data using two primary objectives: masked language modeling (MLM) and next sentence prediction (NSP). MLM involves randomly masking out some of the words in the input and training the model to predict those masked words based on the surrounding context. NSP, on the other hand, tasks the model with predicting whether two given sentences are consecutive in a document. This pretraining allows BERT to learn rich representations of words and sentences. However, the way BERT is used for sentence-level tasks, especially semantic search, introduces a notable limitation. Typically, to generate sentence embeddings with BERT, you would feed the entire sentence into the model and extract the output from the [CLS] token (a special token added at the beginning of each input sequence). Although BERT captures contextualized word representations spectacularly, this method doesn't produce semantically meaningful sentence embeddings directly. The [CLS] token’s output is generally used to classify the entire sequence rather generate optimized embedings. The problem with this approach relates to the inherent structure of BERT training and how it relates to cosine similarity, and efficiency. Applying BERT for semantic similarity directly tends to produce suboptimal results, especially when measured using cosine similarity between [CLS] embeddings of different sentences.
Several factors contribute to BERT's suboptimal performance in semantic similarity tasks when used out-of-the-box. First, BERT was not explicitly trained to produce sentence embeddings that are directly comparable using cosine similarity or other distance metrics. The pretraining objectives, while effective for language understanding, do not incentivize the model to generate embeddings where semantic similarity is reflected in the vector space. The [CLS] token is a summary of the entire sequence but not necessarily optimized for similarity searches. Second, the high dimensionality of BERT's embeddings (typically 768 or 1024 dimensions) can lead to the "curse of dimensionality," where distance metrics become less reliable in distinguishing between similar and dissimilar vectors. This issue is exacerbated when comparing embeddings from sentences of varying lengths, as the [CLS] token might disproportionately represent the information from the earlier parts of longer sentences. Moreover, using BERT directly for semantic similarity involves individually encoding all sentences and then comparing the resulting embeddings. This process can be computationally expensive, especially when dealing with large databases of text. Since this computation has to repeat for every sentence pair it doesn't lend itself very well to searching very large datasets.
The limitations of using BERT directly for semantic similarity highlight the need for specialized techniques that produce meaningful sentence embeddings optimized for search-related tasks. Here's how those embeddings can be used. These embeddings can then be compared using cosine similarity or other distance metrics to identify sentences that are semantically similar. This approach can be used for tasks such as question answering, document retrieval, and paraphrase detection, where understanding the semantic relationship between sentences is crucial. These are the tasks that are most readily addressed and optimized by sentence transformers. In many search tasks, you need fast retrieval of documents of sentences. BERT could be useful as a RAG enhancement step to re-rank retrieved results but the slow inference makes is unusable in many real search use cases.
Sentence Transformers, as the name suggests, are specifically designed to generate meaningful sentence embeddings that capture the semantic meaning of text. These models are typically built on top of pretrained transformer architectures like BERT, RoBERTa, or DistilBERT, but they are fine-tuned using specific objectives tailored for semantic similarity. The key ingredient in Sentence Transformers is the use of siamese or triplet network architectures during training. These network structures are designed to learn embeddings where semantically similar sentences are mapped to nearby points in the vector space, while dissimilar sentences are mapped to distant points. This careful training ensures that the cosine similarity (or other distance metrics) between sentence embeddings accurately reflects the semantic relationship between the corresponding sentences. The models can be fine-tuned in supervised or unsupervised settings depending on data availability. Sentence creation and search queries can then leverage the fine-tuned model in real use cases.
The core of Sentence Transformers lies in the architecture used during fine-tuning. Siamese networks consist of two identical neural networks that share the same weights and biases. During training, two input sentences (e.g., a query and a candidate document) are fed into the two networks, and the resulting embeddings are compared using a similarity metric like cosine similarity. The network is then trained to minimize a loss function that encourages similar sentences to have high similarity scores and dissimilar sentences to have low similarity scores. Triplet networks, on the other hand, use three input sentences: an anchor sentence, a positive sentence (semantically similar to the anchor), and a negative sentence (semantically dissimilar to the anchor). The model is trained to minimize a loss function that encourages the distance between the anchor and positive embeddings to be smaller than the distance between the anchor and negative embeddings, by a certain margin. This forces the model to learn embeddings that effectively discriminate between similar and dissimilar sentences. These special training methods make sentence transformers uniquely suited for sentence encoding.
In addition to Siamese and Triplet networks, Sentence Transformers can be fine-tuned using various training objectives that explicitly target semantic similarity. One popular objective is Multiple Negatives Ranking (MNR), where the model is trained to rank a set of candidate sentences (e.g., search results) based on their relevance to a given query. The model is given a query and a list of candidate documents, where the true relevant documents are considered "positive" examples, and the irrelevant documents are considered "negative" examples. The model is then trained to assign higher similarity scores to the positive documents than to the negative documents. Another common objective is Contrastive Loss, which is used in Siamese networks to directly learn the embedding space. The loss function is designed to minimize the distance between embeddings of similar sentences and maximize the distance between embeddings of dissimilar sentences. These fine-tuning approaches enable Sentence Transformers to learn highly effective representations for semantic search and related tasks. During training, sentence transformers are pushed and pulled so that similar sentences are closer together.
Sentence Transformers offer several key advantages over standard BERT when it comes to search applications. First and foremost, they produce semantically meaningful sentence embeddings that are directly comparable using cosine similarity or other distance metrics. This allows for efficient and accurate retrieval of relevant documents or sentences based on the semantic similarity to the query. This means that you don't have to perform multiple steps like with BERT to find the sentences that are the most mathematically similar. Second, Sentence Transformers are generally more efficient than using BERT for semantic similarity. Since they are specifically trained to produce good sentence embeddings, they often require less computational resources and can be faster to encode sentences, especially when dealing with large datasets. Efficiency is especially true when you are dealing with very large datasets. If you need to search through millions of documents then the speed of calculating and comparing vector embeddings matters a lot. Third, Sentence Transformers can be easily adapted to different domains and languages by fine-tuning them on specific datasets. This allows you to customize the model for your specific search application and improve its performance on relevant data. Finally, the smaller model sizes of some Sentence Transformer variants (like DistilBERT-based models) can make them easier to deploy and run on resource-constrained environments.
One of the most significant advantages of Sentence Transformers is their efficiency and scalability. Unlike BERT, which requires encoding sentences in pairs to calculate similarity, Sentence Transformers allow you to encode sentences independently and then compare the resulting embeddings using simple distance metrics. This drastically reduces the computational complexity, especially when dealing with large databases of text. For example, if you have a database of N sentences and you want to find the most similar sentence to a given query, using BERT would require encoding the query N times and comparing it to each sentence in the database. With Sentence Transformers, you can encode the sentences in the database once and then encode the query only once, significantly reducing the computational cost. This efficiency makes Sentence Transformers much more scalable for search applications where you need to process large volumes of data in real-time. This helps keep latency at a sustainable level and provides great UX .
Empirical studies have consistently demonstrated that Sentence Transformers outperform standard BERT in semantic search and related tasks. These that the training methodologies in Sentence Transformers produces embedding optimizations. In terms of recall, these models are able to find relevant results consistently at an optimal rate. This is due to the way these are trained. Experiments on standard datasets such as the Semantic Textual Similarity Benchmark (STS) have shown that Sentence Transformers achieve significantly higher correlation scores between predicted similarity scores and human judgments of semantic similarity. Furthermore, Sentence Transformers have been shown to be more robust to variations in sentence length and style, making them more reliable in real-world search scenarios. Studies also reveal that Sentence Transformer's vector embeddings of sentences produce clustering of similar meaning ideas which enhances the ability to perform a thorough search and retrieve the desired information.
To illustrate the performance difference, consider a simple search task where we want to retrieve documents that are relevant to a given query. Using BERT, we would encode the query and all the documents in the database and then calculate the cosine similarity between the query embedding and each document embedding. We would then rank the documents based on their similarity scores and return the top-ranked documents as the search results. With Sentence Transformers, we would follow a similar process, but we would use a Sentence Transformer model to encode the query and the documents. Experiments have shown that Sentence Transformers typically achieve higher precision and recall than BERT in this scenario. It provides a larger area under the curve with respect to precision and recall. This means that they are better at retrieving relevant documents and filtering out irrelevant ones. In addition, Sentence Transformers often achieve better results with lower computational resources, making them a more efficient choice for search applications.
Sentence Transformers are particularly well-suited for a wide range of search and information retrieval applications. One common use case is semantic search, where the goal is to retrieve documents or sentences that are semantically similar to a given query. This is in contrast to traditional keyword-based search, which relies on matching keywords between the query and the documents. Semantic search can be used for tasks such as question answering, document retrieval, and information summarization. Another use case is paraphrase detection, where the goal is to identify sentences that express the same meaning but use different words. This can be used for tasks such as plagiarism detection, machine translation, and text simplification. Sentence Transformers can also be used for tasks such as text classification and topic modeling, where the goal is to categorize documents or sentences based on their content.
Question Answering (QA) systems can be significantly enhanced by using Sentence Transformers. In a QA system, the goal is to provide answers to user questions based on a given corpus of text. Sentence Transformers can be used to encode the questions and the relevant passages in the corpus, allowing the system to quickly identify the passages that are most semantically similar to the question. This can greatly improve the accuracy and efficiency of the QA system. For instance, the system can first use Sentence Transformers to retrieve a small set of candidate passages and then use a more sophisticated model (like BERT) to extract the answer from those passages. This hybrid approach combines the efficiency of Sentence Transformers with the accuracy of BERT, resulting in a highly effective QA system. This helps create systems with high throughput and low latency.
In conclusion, while both BERT and Sentence Transformers are powerful tools for NLP, they are designed for different tasks. BERT is excellent for language understanding and contextualized word representations, but it is not optimized for generating semantic sentence embeddings directly. Sentence Transformers, on the other hand, are specifically designed to generate meaningful sentence embeddings tailored for semantic similarity. Due to the optimization via siamese or triplet networks, sentence transformers generate useful vector embeddings that can then be used in traditional information retrieval systems. For search applications where semantic similarity is crucial, Sentence Transformers generally outperform standard BERT in terms of accuracy, efficiency, and scalability. By understanding the strengths and weaknesses of each approach, you can choose the right tool for the job and optimize your search system for maximum performance. The ability to quickly locate pertinent pieces of information and use this information effectively is greatly enhanced by vector embeddings created by sentence transformers.
