fix: add paper

README.md

@@ -114,8 +114,10 @@ language:
 
 # nomic-embed-text-v2-moe: Multilingual Mixture of Experts Text Embeddings
 
+This model was presented in the paper [Training Sparse Mixture Of Experts Text Embedding Models](https://huggingface.co/papers/2502.07972).
+
 ## Model Overview
-`nomic-embed-text-v2-moe` is SoTA multilingual MoE text embedding model that excels at multilingual retrieval:
+`nomic-embed-text-v2-moe` is a SoTA multilingual MoE text embedding model that excels at multilingual retrieval:
 
 - **High Performance**: SoTA Multilingual performance compared to ~300M parameter models, competitive with models 2x in size
 - **Multilinguality**: Supports ~100 languages and trained on over 1.6B pairs
@@ -134,7 +136,9 @@ language:
 | Arctic Embed v2 Large | 568 | 1024 | **55.65** | 66.00 | ❌ | ❌ | ❌ |
 | mE5 Large | 560 | 1024 | 51.40 | 66.50 | ❌ | ❌ | ❌ |
 
+## Paper Abstract
 
+Transformer-based text embedding models have improved their performance on benchmarks like MIRACL and BEIR by increasing their parameter counts. However, this scaling approach introduces significant deployment challenges, including increased inference latency and memory usage. These challenges are particularly severe in retrieval-augmented generation (RAG) applications, where large models' increased memory requirements constrain dataset ingestion capacity, and their higher latency directly impacts query-time performance. While causal language models have addressed similar efficiency challenges using Mixture of Experts (MoE) architectures, this approach hasn't been successfully adapted to the general text embedding setting. In this paper, we introduce Nomic Embed v2, the first general purpose MoE text embedding model. Our model outperforms models in the same parameter class on both monolingual and multilingual benchmarks while also maintaining competitive performance with models twice its size. We open-source all code, models, and evaluation data to ensure full reproducibility of our training pipeline at https://github.com/nomic-ai/contrastors.
 
 ## Model Architecture
 - **Total Parameters**: 475M
@@ -270,4 +274,4 @@ If you find the model, dataset, or training code useful, please cite our work
       primaryClass={cs.CL},
       url={https://arxiv.org/abs/2502.07972}, 
 }
-```
+```