nomic embed vision v1 EU AI Act Compliance Profile

nomic-embed-vision-v1: Expanding the Latent Space

nomic-embed-vision-v1 is a high performing vision embedding model that shares the same embedding space as nomic-embed-text-v1.

All Nomic Embed Text models are now multimodal!

| Name | Imagenet 0-shot | Datacomp (Avg. 38) | MTEB | | :-------------------------------:| :-------------- | :----------------- | :------: | | nomic-embed-vision-v1.5 | 71.0 | 56.8 | 62.28 | | nomic-embed-vision-v1 | 70.7 | 56.7 | 62.39 | | OpenAI CLIP ViT B/16 | 68.3 | 56.3 | 43.82 | | Jina CLIP v1 | 59.1 | 52.2 | 60.1 |

Hosted Inference API

The easiest way to get started with Nomic Embed is through the Nomic Embedding API.

Generating embeddings with the nomic Python client is as easy as

from nomic import embed
import numpy as np

output = embed.image(
    images=[
        "image_path_1.jpeg",
        "image_path_2.png",
    ],
    model='nomic-embed-vision-v1',
)

print(output['usage'])
embeddings = np.array(output['embeddings'])
print(embeddings.shape)

For more information, see the API reference

Data Visualization

Click the Nomic Atlas map below to visualize a 100,000 sample CC3M comparing the Vision and Text Embedding Space!

Training Details

We align our vision embedder to the text embedding by employing a technique similar to LiT but instead lock the text embedder!

For more details, see the Nomic Embed Vision Technical Report (soon to be

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