README.md

---
base_model:
- nomic-ai/nomic-embed-text-v2-moe-unsupervised
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
license: apache-2.0
language:
- en
- es
- fr
- de
- it
- pt
- pl
- nl
- tr
- ja
- vi
- ru
- id
- ar
- cs
- ro
- sv
- el
- uk
- zh
- hu
- da
- 'no'
- hi
- fi
- bg
- ko
- sk
- th
- he
- ca
- lt
- fa
- ms
- sl
- lv
- mr
- bn
- sq
- cy
- be
- ml
- kn
- mk
- ur
- fy
- te
- eu
- sw
- so
- sd
- uz
- co
- hr
- gu
- ce
- eo
- jv
- la
- zu
- mn
- si
- ga
- ky
- tg
- my
- km
- mg
- pa
- sn
- ha
- ht
- su
- gd
- ny
- ps
- ku
- am
- ig
- lo
- mi
- nn
- sm
- yi
- st
- tl
- xh
- yo
- af
- ta
- tn
- ug
- az
- ba
- bs
- dv
- et
- gl
- gn
- gv
- hy
---

# nomic-embed-text-v2-moe: Multilingual Mixture of Experts Text Embeddings

## Model Overview
`nomic-embed-text-v2-moe` is 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
- **Flexible Embedding Dimension**: Trained with [Matryoshka Embeddings](https://arxiv.org/abs/2205.13147) with 3x reductions in storage cost with minimal performance degradations
- **Fully Open-Source**: Model weights, [code](https://github.com/nomic-ai/contrastors), and training data (see code repo) released


| Model | Params (M) | Emb Dim | BEIR | MIRACL | Pretrain Data | Finetune Data | Code |
|-------|------------|----------|------|---------|---------------|---------------|------|
| **Nomic Embed v2** | 305 | 768 | 52.86 | **65.80** | ✅ | ✅ | ✅ |
| mE5 Base | 278 | 768 | 48.88 | 62.30 | ❌   | ❌   | ❌   |
| mGTE Base | 305 | 768 | 51.10 | 63.40 | ❌ | ❌ | ❌ |
| Arctic Embed v2 Base | 305 | 768 | **55.40** | 59.90 | ❌ | ❌ | ❌ |
|   |
| BGE M3 | 568 | 1024 | 48.80 | **69.20** | ❌ | ✅ | ❌ |
| Arctic Embed v2 Large | 568 | 1024 | **55.65** | 66.00 | ❌ | ❌ | ❌ |
| mE5 Large | 560 | 1024 | 51.40 | 66.50 | ❌ | ❌ | ❌ |



## Model Architecture
- **Total Parameters**: 475M
- **Active Parameters During Inference**: 305M
- **Architecture Type**: Mixture of Experts (MoE)
- **MoE Configuration**: 8 experts with top-2 routing
- **Embedding Dimensions**: Supports flexible dimension from 768 to 256 through Matryoshka representation learning
- **Maximum Sequence Length**: 512 tokens
- **Languages**: Supports dozens of languages (see Performance section)


## Usage Guide

### Installation

The model can be used through SentenceTransformers and Transformers.

For best performance on GPU, please install

```bash
pip install torch transformers einops git+https://github.com/nomic-ai/megablocks.git
```

> [!IMPORTANT]
> **Important!**
> The text prompt *must* include a *task instruction prefix*, instructing the model which task is being performed. 

Please use `search_query: ` before your queries/questions, and `search_document: ` before your documents.

### Transformers

If using Transformers, **make sure to prepend the task instruction prefix**.

```python
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("nomic-ai/nomic-embed-text-v2-moe")
model = AutoModel.from_pretrained("nomic-ai/nomic-embed-text-v2-moe", trust_remote_code=True)

sentences = ['search_document: Hello!', 'search_document: ¡Hola!']

def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
model.eval()
with torch.no_grad():
    model_output = model(**encoded_input)
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)
print(embeddings.shape)
# torch.Size([2, 768])

similarity = F.cosine_similarity(embeddings[0], embeddings[1], dim=0)
print(similarity)
# tensor(0.9118)
```

### SentenceTransformers

With SentenceTransformers, you can specify the `prompt_name` as either `"query"` or `"passage"`, and the task instruction will be included automatically.

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/nomic-embed-text-v2-moe", trust_remote_code=True)
sentences = ["Hello!", "¡Hola!"]
embeddings = model.encode(sentences, prompt_name="passage")
print(embeddings.shape)
# (2, 768)

similarity = model.similarity(embeddings[0], embeddings[1])
print(similarity)
# tensor([[0.9118]])
```

## Performance

nomic-embed-text-v2-moe performance on BEIR and MIRACL compared to other open-weights embedding models:

![image/png](https://cdn-uploads.huggingface.co/production/uploads/607997c83a565c15675055b3/xadjrezEIM0Q1jbgmjqO7.png)

nomic-embed-text-v2-moe performance on BEIR at 768 dimension and truncated to 256 dimensions:

![image/png](https://cdn-uploads.huggingface.co/production/uploads/607997c83a565c15675055b3/8hmhWQ_TTmlrviZFIBSxo.png)

## Best Practices
- Add appropriate prefixes to your text:
  - For queries: "search_query: "
  - For documents: "search_document: "
- Maximum input length is 512 tokens
- For optimal efficiency, consider using the 256-dimension embeddings if storage/compute is a concern

## Limitations
- Performance may vary across different languages
- Resource requirements may be higher than traditional dense models due to MoE architecture
- Must use `trust_remote_code=True` when loading the model to use our custom architecture implementation

## Training Details

![image/png](https://cdn-uploads.huggingface.co/production/uploads/607997c83a565c15675055b3/F0lyAtV8wXMBmxSbtIgL4.png)

- Trained on 1.6 billion high-quality pairs across multiple languages
- Uses consistency filtering to ensure high-quality training data
- Incorporates Matryoshka representation learning for dimension flexibility
- Training includes both weakly-supervised contrastive pretraining and supervised finetuning

For more details, please check out the [blog post](https://www.nomic.ai/blog/posts/nomic-embed-text-v2) and [technical report](https://www.arxiv.org/abs/2502.07972).



## Join the Nomic Community

- Nomic: [https://nomic.ai](https://nomic.ai)
- Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8)
- Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai)

# Citation

If you find the model, dataset, or training code useful, please cite our work

```bibtex
@misc{nussbaum2025trainingsparsemixtureexperts,
      title={Training Sparse Mixture Of Experts Text Embedding Models}, 
      author={Zach Nussbaum and Brandon Duderstadt},
      year={2025},
      eprint={2502.07972},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.07972}, 
}
```