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--- |
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library_name: transformers |
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language: |
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- en |
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pipeline_tag: image-feature-extraction |
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license: cc-by-nc-4.0 |
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inference: false |
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--- |
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# nomic-embed-vision-v1: Expanding the Latent Space |
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`nomic-embed-vision-v1` is a high performing vision embedding model that shares the same embedding space as [nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1). |
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All Nomic Embed Text models are now **multimodal**! |
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| Name | Imagenet 0-shot | Datacomp (Avg. 38) | MTEB | |
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| :-------------------------------:| :-------------- | :----------------- | :------: | |
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| `nomic-embed-vision-v1.5` | **71.0** | **56.8** | 62.28 | |
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| `nomic-embed-vision-v1` | 70.7 | 56.7 | **62.39** | |
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| OpenAI CLIP ViT B/16 | 68.3 | 56.3 | 43.82 | |
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| Jina CLIP v1 | 59.1 | 52.2 | 60.1 | |
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## Hosted Inference API |
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The easiest way to get started with Nomic Embed is through the Nomic Embedding API. |
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Generating embeddings with the `nomic` Python client is as easy as |
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```python |
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from nomic import embed |
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import numpy as np |
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output = embed.image( |
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images=[ |
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"image_path_1.jpeg", |
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"image_path_2.png", |
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], |
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model='nomic-embed-vision-v1', |
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) |
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print(output['usage']) |
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embeddings = np.array(output['embeddings']) |
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print(embeddings.shape) |
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``` |
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For more information, see the [API reference](https://docs.nomic.ai/reference/endpoints/nomic-embed-vision) |
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## Data Visualization |
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Click the Nomic Atlas map below to visualize a 100,000 sample CC3M comparing the Vision and Text Embedding Space! |
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[](https://atlas.nomic.ai/data/nomic-multimodal-series/cc3m-100k-image-bytes-v15/map) |
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## Training Details |
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We align our vision embedder to the text embedding by employing a technique similar to [LiT](https://arxiv.org/abs/2111.07991) but instead lock the text embedder! |
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For more details, see the Nomic Embed Vision Technical Report (soon to be released!) and corresponding [blog post](https://blog.nomic.ai/posts/nomic-embed-vision) |
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Training code is released in the `contrastors` [repository](https://github.com/nomic-ai/contrastors) |
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## Usage |
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Remember `nomic-embed-text` *requires* prefixes and so, when using Nomic Embed in multimodal RAG scenarios (e.g. text to image retrieval), |
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you should use the `search_query: ` prefix. |
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### Transformers |
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```python |
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import torch |
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import torch.nn.functional as F |
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from transformers import AutoTokenizer, AutoModel, AutoImageProcessor |
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from PIL import Image |
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import requests |
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processor = AutoImageProcessor.from_pretrained("nomic-ai/nomic-embed-vision-v1") |
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vision_model = AutoModel.from_pretrained("nomic-ai/nomic-embed-vision-v1", trust_remote_code=True) |
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url = 'http://images.cocodataset.org/val2017/000000039769.jpg' |
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image = Image.open(requests.get(url, stream=True).raw) |
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inputs = processor(image, return_tensors="pt") |
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img_emb = vision_model(**inputs).last_hidden_state |
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img_embeddings = F.normalize(img_emb[:, 0], p=2, dim=1) |
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``` |
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Additionally, you can perform multimodal retrieval! |
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```python |
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def mean_pooling(model_output, attention_mask): |
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token_embeddings = model_output[0] |
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
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sentences = ['search_query: What are cute animals to cuddle with?', 'search_query: What do cats look like?'] |
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tokenizer = AutoTokenizer.from_pretrained('nomic-ai/nomic-embed-text-v1') |
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text_model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True) |
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text_model.eval() |
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
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with torch.no_grad(): |
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model_output = text_model(**encoded_input) |
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text_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
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text_embeddings = F.normalize(text_embeddings, p=2, dim=1) |
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print(torch.matmul(img_embeddings, text_embeddings.T)) |
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``` |
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# Join the Nomic Community |
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- Nomic: [https://nomic.ai](https://nomic.ai) |
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- Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8) |
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- Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai) |
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