# MedEIR: A Specialized Medical Embedding Model for Enhanced Information Retrieval

arXiv: https://arxiv.org/abs/2505.13482 · HTML: https://mlsanity.com/paper/2505.13482/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 12 May 2025.

## Placed in

- Retrieval-augmented generation / [Medical and biomedical RAG](https://mlsanity.com/rag/medical-and-biomedical-rag/)

## Builds on (most cited first, 9 of 9)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation](https://mlsanity.com/paper/2108.12409/) (2021)
- [Nomic Embed: Training a Reproducible Long Context Text Embedder](https://mlsanity.com/paper/2402.01613/) (2024)

## Abstract (arXiv)

Embedding models have become essential for retrieval-augmented generation (RAG) tasks, semantic clustering, and text re-ranking. But despite their growing use, many of these come with notable limitations. For example, Jina fails to capture the semantic content of medical documents, while models such as MiniLM often perform poorly on long-form documents. Domain-adapted models, while specialized, often underperform in general-purpose tasks, reducing their overall applicability. General-domain tokenizers often misinterpret medical vocabulary. The limitations of current embedding models, whether in tokenization accuracy, domain comprehension, or handling long sequences, highlight the need for more versatile solutions. In this work, we present MedEIR, a novel embedding model and tokenizer jointly optimized for both medical and general NLP tasks, incorporating ALiBi-based long-context processing to support sequences of up to 8,192 tokens. MedEIR was pre-trained on only 6 billion tokens, significantly fewer than Jina's, followed by fine-tuning on 3 million sentence pairs. MedEIR consistently outperforms Jina V2 and MiniLM across MTEB benchmarks, achieving top scores on ArguAna (55.24), NFCorpus (38.44), MedicalQARetrieval (74.25), SciFact (72.04), and TRECCOVID (79.56). These results highlight the potential of MedEIR as a highly effective embedding model, demonstrating strong performance across both general-purpose and domain-specific tasks and outperforming existing models on multiple benchmarks.