nomic-embed-text-v2-moe

nomic-embed-text-v2-moe:latest

956.3K Downloads Updated 9 months ago

nomic-embed-text-v2-moe is a multilingual MoE text embedding model that excels at multilingual retrieval.

embedding

ollama pull nomic-embed-text-v2-moe

curl http://localhost:11434/api/embed \
  -d '{
    "model": "nomic-embed-text-v2-moe",
    "input": "Why is the sky blue?"
  }'

import ollama

response = ollama.embed(
    model='nomic-embed-text-v2-moe',
    input='The sky is blue because of Rayleigh scattering',
)
print(response.embeddings)

import ollama from 'ollama'

const response = await ollama.embed({
  model: 'nomic-embed-text-v2-moe',
  input: 'The sky is blue because of Rayleigh scattering',
})
console.log(response.embeddings)

Details

Updated 9 months ago

9 months ago

ff9c2f10ef5e · 958MB

model

archnomic-bert-moe

·

parameters475M

·

quantizationF16

958MB

Readme

nomic-embed-text-v2-moe is a 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 with 3x reductions in storage cost with minimal performance degradations
Fully Open-Source: Model weights, code, and training data

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	❌	❌	❌

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

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: See below for supported languages and its training pairs per different languages

`nomic-embed-text-v2-moe` is a 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

| 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 | ❌ | ❌ | ❌ |

### 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

### 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**: See below for supported languages and its training pairs per different languages

![image.png](/assets/library/nomic-embed-text-v2-moe/f235fb9c-f5ee-407c-aaa1-54ea35d95e0e)

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