How can I reduce the storage and compute costs of embeddings?

Matryoshka embeddings allow you to compress embedding dimensions with minimal performance loss, saving significant storage (up to 94%) and compute.

Which embedding models offer reproducible training and open-source resources?

Nomic Embed is highlighted for its open-source code, data, and training process, ensuring full reproducibility. It's based on BERT and useful for understanding the training methodology.

Are there embedding models specifically for code?

Currently, most embedding models are general-purpose. Companies like Codium and Cursor have developed proprietary code embedding models, but as of now, there's a lack of publicly available, specialized code embedding models.

What are LoRA adapters in the context of embeddings?

Jina Embed introduces LoRA adapters, which are task-specific components that attach to embedding models. This allows for specialized embeddings optimized for tasks like document retrieval or text matching, moving beyond single, general-purpose models.

Are there advancements in multilingual embedding models?

Jina, as a European company, focuses heavily on multilingualism, supporting 89 languages. They offer practical models and insights into scaling laws for cross-language transfer.

What are contextual embeddings and why are they efficient?

Contextual embeddings use a two-stage adaptation process: first conditioning the model on a specific corpus, then using it for embeddings. This allows much smaller models (e.g., 143 million parameters) to compete with larger ones (e.g., 7 billion parameters) due to domain adaptation.

Can I use embeddings for specialized domains like biomedical data?

While general-purpose embeddings exist, there's a need for specialized biomedical embedding models. While some models like BioBERT exist, their effectiveness varies. The Nomic code might be adaptable for biomedical data, or fine-tuning generic models is another option.

Key Moments

[Paper Club] Embeddings in 2024: OpenAI, Nomic Embed, Jina Embed, cde-small-v1 - with swyx

Latent Space Podcast

Science & Technology3 min read42 min video

Dec 1, 2024|1,228 views|31|2

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Key Moments

TL;DR

2024 embeddings overview: OpenAI, Nomic, Jina, and Contextual Document Embeddings (CDE).

Key Insights

MTB is the go-to benchmark for embeddings, highlighting tradeoffs between model size, efficiency, and performance.

OpenAI's text-embedding-3 offers multiple sizes and introduces Matryoshka embeddings for efficient dimension reduction with minimal performance loss.

Nomic Embed provides a fully reproducible framework for training embedding models, emphasizing open source code and data.

Jina Embed V3 focuses on multilinguality and introduces task-specific LoRA adapters to optimize embeddings for different tasks like retrieval and classification.

Contextual Document Embeddings (CDE) utilize a two-stage adaptation process for increased efficiency and domain adaptation, achieving high performance with smaller models.

The development of specialized embedding models for domains like code is currently lacking, with most models being general-purpose.

THE EMBEDDING LANDSCAPE AND KEY BENCHMARKS

The current landscape of embeddings is marked by a rapid evolution of models and evaluation methodologies. The Massive Text Embedding Benchmark (MTB) serves as a de facto standard for assessing embedding performance. While criticisms exist, it's crucial for understanding the relative strengths of various models. The benchmark's evolution shows a shift in dominance, with both American and Chinese models now prominent. Importantly, MTB highlights the critical trade-offs between model size, memory usage, and performance, which are paramount for practical deployment and efficiency.

OPENAI'S EMBEDDING OFFERINGS AND MATRYOSHKA

OpenAI's embedding models, often a starting point due to existing API access, remain highly competitive. For the first time, they offer distinct model sizes, catering to different needs. A significant innovation is Matryoshka embeddings, which allow for substantial dimension reduction—compressing from 1024 to 64 dimensions, for example—with a minimal drop in performance. This technique drastically reduces storage and compute requirements, making embeddings more practical for production environments where latency is a concern.

NOMIC EMBED: REPRODUCIBILITY AND TRAINING PROCESSES

Nomic Embed stands out for its commitment to full reproducibility, offering open-source code, data, and training methodologies. This makes it an excellent resource for those wanting to deep-dive into the embedding model training process. Their approach utilizes standard, state-of-the-art training techniques, including modifications to the masking strategy. A key observation is that many models are essentially updated versions of BERT, suggesting that data quality and selection play a crucial role in performance, alongside architectural advancements.

JINA EMBED V3: MULTILINGUALITY AND TASK-SPECIFIC ADAPTERS

Jina Embed V3, developed by a European company, emphasizes multilinguality, supporting 89 languages. They offer practical insights into scaling laws and cross-language transfer data sets. A notable advancement is the introduction of task-specific LoRA adapters. These adapters allow for specialized embeddings tailored to different tasks such as document retrieval, query processing, clustering, and classification, moving beyond traditional single-model approaches and significantly boosting performance for specific applications.

CONTEXTUAL DOCUMENT EMBEDDINGS (CDE): EFFICIENT DOMAIN ADAPTATION

Contextual Document Embeddings (CDE) present a novel two-stage adaptation process designed for enhanced efficiency and domain adaptation. This method involves an initial phase of conditioning the model on a specific corpus, followed by a second stage for embedding context. Even with a much smaller model size (143 million parameters compared to 7 billion), CDE can outperform larger models on various tasks. This approach offers a significant efficiency win, though its stateful API deployment might require adaptation from existing stateless systems.

THE GAP IN SPECIALIZED DOMAIN EMBEDDINGS

A surprising observation in the current embedding landscape is the lack of specialized models for domains like coding. While general-purpose language models are prevalent, companies like Codium and Cursor have had to develop proprietary code embedding models. This presents an opportunity for research into domain-specific embeddings, potentially by adapting general frameworks like Nomic's via dataset swapping or by fine-tuning existing models, which is generally favored over training from scratch.

Mentioned in This Episode

●Software & Apps

●Companies

●Concepts

Common Questions

The Massive Text Embedding Benchmark (MT-Bench) is considered the de facto standard. While it has criticisms, understanding it is crucial for anyone working with embeddings.

Topics

AI & Machine Learning Technology & Innovation Programming & Software Deep Learning Natural Language Processing Embedding Models Model Efficiency Information Retrieval Multilingual AI

Mentioned in this video

Concepts

Massive Text Embedding Benchmark

A de facto benchmark for evaluating text embedding models, with criticisms but considered essential knowledge for anyone using embeddings.

Contextual Embeddings

A technique involving a two-stage adaptation process: first conditioning the model on a corpus and then using it for embeddings, notably improving efficiency even with smaller models.

LoRA Adapters

Task-specific adapters introduced by Jina, allowing for different embedding models tailored to specific tasks like document retrieval, query retrieval, or text matching, moving beyond single embedding models.

Matryoshka Embeddings

A technique allowing for the reduction of embedding dimensions, significantly saving storage space and compute with a minimal performance drop. OpenAI was the first to acknowledge its relevance.

Software & Apps

Jina Embed

A European company focused on multilingual embedding models. Their Jina Clip 2 was noted for out-of-the-box deployability and their embeddings were updated in September, focusing on multilingual capabilities.

CLIP

A multimodal model that integrates vision and text embeddings. The speaker highlights its utility and provides qualitative examples comparing its performance to OpenAI's Clip.

BioBERT

A BERT-based model from Alibaba discussed in the context of biomedical embeddings, found to be not ideal for a specific medical paper retrieval use case.

Nomic Embed

A model discussed for its open-source code, data, and training process, aiming for full reproducibility. It uses BERT architecture and is associated with Nomic Atlas, a visualization tool.

Med-PaLM

A large language model from Google focused on healthcare, mentioned briefly in the context of medical AI.

BERT

The architecture used by Nomic Embed, noted as a standard in training processes. The speaker expressed surprise that models are still largely updated versions of BERT.

Nomic Atlas

A cluster visualization tool developed by Nomic, which motivates their investment in embedding tools for data exploration.

People

Sam

A model for image segmentation. A specific medical imaging variant exists, and it has been fine-tuned for medical applications, though not a foundation model for general embeddings.

Companies

OpenAI

Mentioned for their embedding offerings, which are typically a starting point due to existing API keys, though not always the best performing. They were the first to acknowledge the relevance of Matryoshka embeddings.

John Snow Labs

An active organization in the biomedical embedding space, mentioned during a discussion about models trained on biomedical data.

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