How to Choose the Right Embedding Model?
Embeddings have become a cornerstone of modern AI, helping machines understand text, images, sounds, and even graphs by turning them into vector representations. With so many options available, picking the right embedding model can feel overwhelming. Here’s a guide to help you make the right choice.
1. Identify Your Data Type
Start with the basics: what kind of data are you working with?
- Text: Consider word, sentence, or document embeddings (e.g., Word2Vec, BERT).
- Images: Use models that create image embeddings from pixels (e.g., CNN-based or Vision Transformers).
- Audio: Look for audio or speech embeddings for tasks like speaker identification.
- Graphs: Try graph embedding methods to capture relationships in networks.
- Multimodal: Need to handle images and text together? Consider multimodal embeddings like CLIP.
Choosing an embedding model that fits your data type narrows down your options significantly.
2. Consider Contextual vs. Static Embeddings
If you’re dealing with text, you have two main choices:
- Static Embeddings (Word2Vec, GloVe, FastText): Also known as non-contextual embeddings. Simpler and fast but don’t understand context well.
- Contextual Embeddings (BERT, GPT-based, Sentence-BERT): More complex but better at handling ambiguous language and adapting to different contexts.
If your task requires deep language understanding, go for contextual embeddings. To illustrate why contextuality matters, consider the word “apple” in the following sentences:
Apple released the new version of MacOS.
I ate an apple this morning.
A static embedding model will produce the same representation for the word “apple” in both sentences. In contrast, a contextual embedding model will cluster the first “Apple” (the company) closer to tech-related terms and other technology brands, while it will cluster the second “apple” (the fruit) closer to other fruits.
As a rule of thumb, if the contextual information in your textual data is not strongly correlated with the task at hand, use lightweight models like FastText. Otherwise, consider transformer-based models to take advantage of the contextual information they provide.
3. Pre-Trained vs. Custom Models
- Pre-Trained Models: Models like BERT, CLIP, or ResNet are readily available and work well out-of-the-box. They’re great if you have limited data or time. Sentence-transformers has a great collection of pre-trained embedding models. You can also find many pre-trained open-source models in HuggingFace Model Hub for all kind of specific needs and languages.
- Custom Models: If you’re in a niche domain (e.g., legal or medical text), fine-tuning a pre-trained model or training your own may yield better results.
Try a pre-trained model first. If it’s not good enough, consider customizing it with your own data.
4. Balance Performance and Practicality
Complex, large models often perform better but may require more computing power. If you’re resource-constrained, pick a simpler or a smaller model. Consider how fast you need the model to run and how much hardware you have available.
Be aware of that different models have different maximum sequence length values. If your text data contains long paragraphs, you need a model with a large context length. Similarly, models’ output dimension can vary from 300 up to 4096 depending on the model. These are significant factors to consider if you are working with a large dataset, or if your task includes time or computation constraints.
5. Match the Model to Your Task
Some embeddings are better suited for specific tasks. For example, sentence-transformer models are better at semantic search, while CLIP is ideal for linking images and text. Keep in mind that a model’s performance can vary depending on factors such as the task type, language, and training data.
In general, models trained specifically for semantic similarity tasks tend to produce stronger overall representations. However, if you have unique domain requirements, a model trained directly on your in-domain data may yield better results.
Before making a decision, review literature or community benchmarks to see which models perform best on tasks similar to yours. The MTEB leaderboard is a great starting point for comparing different options.
6. Test Before Committing
Don’t just trust blog posts or papers — test a few candidate models on a small sample of your data.
- Intrinsic Tests: Check if similar items cluster well.
- Extrinsic Tests: Measure how embeddings affect your final metrics, like accuracy or precision in your downstream task.
A quick pilot test can save you time and help you pick the best model.
7. Last Notes
Choose an embedding model that aligns with your data type, complexity, and downstream tasks. Start with pre-trained models and evaluate their performance; only consider custom solutions if you genuinely need them. Keep an eye on resource requirements and future scalability.
While transformer-based models (like BERT) are popular and powerful, remember that sometimes a simple approach (like TF-IDF) might be sufficient and far more resource-efficient. Don’t let the allure of using the latest, most complex model lead you to over-engineer a solution. Instead, pick the simplest effective method that meets your needs.
References
MTEB leaderboard — a hugging face space by mteb. Available at: https://huggingface.co/spaces/mteb/leaderboard (Accessed: 18 December 2024).
Sentencetransformers documentation. Available at: https://sbert.net/ (Accessed: 18 December 2024).
