nomic-ai
/

nomic-embed-vision-v1

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 library_name: transformers
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 ---
 library_name: transformers
+language:
+- en
+pipeline_tag: image-feature-extraction
+license: cc-by-nc-4.0
+inference: false
 ---
+# 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](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5).
+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
+```python
+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](https://docs.nomic.ai/reference/endpoints/nomic-embed-vision)
+## Data Visualization
+Click the Nomic Atlas map below to visualize a 100,000 sample CC3M comparing the Vision and Text Embedding Space!
+[![image/webp](https://cdn-uploads.huggingface.co/production/uploads/607997c83a565c15675055b3/aKJogjDQ4BBiYGRIIrFMa.webp)](https://atlas.nomic.ai/data/nomic-multimodal-series/cc3m-100k-image-bytes-v15/map)
 ## Training Details
+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!
+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)
+Training code is released in the `contrastors` [repository](https://github.com/nomic-ai/contrastors)
+## Usage
+Note `nomic-embed-text` *requires* prefixes! We support the prefixes `[search_query, search_document, classification, clustering]`.
+For retrieval applications, you should prepend `search_document` for all your documents and `search_query` for your queries.
+For example, you are building a RAG application over the top of Wikipedia. You would embed all Wikipedia articles with the prefix `search_document`
+and any questions you ask with `search_query`. For example:
+```python
+queries = ["search_query: who is the first president of the united states?", "search_query: when was babe ruth born?"]
+documents = ["search_document: <article about US Presidents>", "search_document: <article about Babe Ruth>"]
+```
+You can
+### Transformers
+```python
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModel, AutoImageProcessor
+from PIL import Image
+import requests
+processor = AutoImageProcessor.from_pretrained("nomic-ai/nomic-embed-vision-v1")
+vision_model = AutoModel.from_pretrained("nomic-ai/nomic-embed-vision-v1", trust_remote_code=True)
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
+inputs = processor(image, return_tensors="pt")
+img_emb = vision_model(**inputs).last_hidden_state
+img_embeddings = F.normalize(img_emb[:, 0], p=2, dim=1)
+```
+Additionally, you can perform multimodal retrieval!
+```python
+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)
+sentences = ['search_query: What are cute animals to cuddle with?', 'search_query: What do cats look like?']
+tokenizer = AutoTokenizer.from_pretrained('nomic-ai/nomic-embed-text-v1')
+text_model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True)
+text_model.eval()
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+with torch.no_grad():
+    model_output = text_model(**encoded_input)
+text_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+text_embeddings = F.normalize(text_embeddings, p=2, dim=1)
+print(torch.matmul(img_embeddings, text_embeddings.T))
+```
+# 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)