README.md

---
tags:
- mmeb
- transformers
- sentence-transformers
language:
- en
- ar
- zh
- ko
- ru
- pl
- tr
- fr
library_name: transformers
license: mit
pipeline_tag: zero-shot-image-classification
---

## mmE5-mllama-11b-instruct

[mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data](https://arxiv.org/abs/2502.08468.pdf). Haonan Chen, Liang Wang, Nan Yang, Yutao Zhu, Ziliang Zhao, Furu Wei, Zhicheng Dou, arXiv 2025

This model is trained based on [Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision).

[Github](https://github.com/haon-chen/mmE5)

## Train/Eval Data
 - Train data: https://huggingface.co/datasets/intfloat/mmE5-MMEB-hardneg, https://huggingface.co/datasets/intfloat/mmE5-synthetic
 - Eval data: https://huggingface.co/datasets/TIGER-Lab/MMEB-eval, https://huggingface.co/datasets/Haon-Chen/XTD-10

## Experimental Results
Our model achieves SOTA performance on MMEB benchmark.
<img width="900" alt="abs" src="https://raw.githubusercontent.com/haon-chen/mmE5/refs/heads/main/figures//exp_result.jpg">

## Usage

### Transformers

Below is an example we adapted from [VLM2Vec](https://huggingface.co/TIGER-Lab/VLM2Vec-Full).

```python
import torch
import requests

from PIL import Image
from transformers import MllamaForConditionalGeneration, AutoProcessor

# Pooling and Normalization
def last_pooling(last_hidden_state, attention_mask, normalize=True):
    sequence_lengths = attention_mask.sum(dim=1) - 1
    batch_size = last_hidden_state.shape[0]
    reps = last_hidden_state[torch.arange(batch_size, device=last_hidden_state.device), sequence_lengths]
    if normalize:
        reps = torch.nn.functional.normalize(reps, p=2, dim=-1)
    return reps

def compute_similarity(q_reps, p_reps):
    return torch.matmul(q_reps, p_reps.transpose(0, 1))

model_name = "intfloat/mmE5-mllama-11b-instruct"

# Load Processor and Model
processor = AutoProcessor.from_pretrained(model_name)
model = MllamaForConditionalGeneration.from_pretrained(
    model_name, torch_dtype=torch.bfloat16
).to("cuda")
model.eval()

# Image + Text -> Text
image = Image.open(requests.get('https://github.com/haon-chen/mmE5/blob/main/figures/example.jpg?raw=true', stream=True).raw)
inputs = processor(text='<|image|><|begin_of_text|>Represent the given image with the following question: What is in the image\n', images=[image], return_tensors="pt").to("cuda")
qry_output = last_pooling(model(**inputs, return_dict=True, output_hidden_states=True).hidden_states[-1], inputs['attention_mask'])

string = 'A cat and a dog'
text_inputs = processor(text=string, return_tensors="pt").to("cuda")
tgt_output = last_pooling(model(**text_inputs, return_dict=True, output_hidden_states=True).hidden_states[-1], text_inputs['attention_mask'])
print(string, '=', compute_similarity(qry_output, tgt_output))
## A cat and a dog = tensor([[0.4219]], device='cuda:0', dtype=torch.bfloat16)

string = 'A cat and a tiger'
text_inputs = processor(text=string, return_tensors="pt").to("cuda")
tgt_output = last_pooling(model(**text_inputs, return_dict=True, output_hidden_states=True).hidden_states[-1], text_inputs['attention_mask'])
print(string, '=', compute_similarity(qry_output, tgt_output))
## A cat and a tiger = tensor([[0.3184]], device='cuda:0', dtype=torch.bfloat16)

# Text -> Image
inputs = processor(text='Find me an everyday image that matches the given caption: A cat and a dog.\n', return_tensors="pt").to("cuda")
qry_output = last_pooling(model(**inputs, return_dict=True, output_hidden_states=True).hidden_states[-1], inputs['attention_mask'])

string = '<|image|><|begin_of_text|>Represent the given image.\n'
tgt_inputs = processor(text=string, images=[image], return_tensors="pt").to("cuda")
tgt_output = last_pooling(model(**tgt_inputs, return_dict=True, output_hidden_states=True).hidden_states[-1], tgt_inputs['attention_mask'])
print(string, '=', compute_similarity(qry_output, tgt_output))
## <|image|><|begin_of_text|>Represent the given image. = tensor([[0.4414]], device='cuda:0', dtype=torch.bfloat16)

inputs = processor(text='Find me an everyday image that matches the given caption: A cat and a tiger.\n', return_tensors="pt").to("cuda")
qry_output = last_pooling(model(**inputs, return_dict=True, output_hidden_states=True).hidden_states[-1], inputs['attention_mask'])
string = '<|image|><|begin_of_text|>Represent the given image.\n'
tgt_inputs = processor(text=string, images=[image], return_tensors="pt").to("cuda")
tgt_output = last_pooling(model(**tgt_inputs, return_dict=True, output_hidden_states=True).hidden_states[-1], tgt_inputs['attention_mask'])
print(string, '=', compute_similarity(qry_output, tgt_output))
## <|image|><|begin_of_text|>Represent the given image. = tensor([[0.3730]], device='cuda:0', dtype=torch.bfloat16)
```

### Sentence Transformers

You can also use Sentence Transformers, where the majority of the pre- and post-processing has been abstracted.

```python
from sentence_transformers import SentenceTransformer
import requests

# Load the model
model = SentenceTransformer("intfloat/mmE5-mllama-11b-instruct", trust_remote_code=True)

# Download an example image of a cat and a dog
dog_cat_image_bytes = requests.get('https://github.com/haon-chen/mmE5/blob/main/figures/example.jpg?raw=true', stream=True).raw.read()
with open("cat_dog_example.jpg", "wb") as f:
    f.write(dog_cat_image_bytes)

# Image + Text -> Text
image_embeddings = model.encode([{
    "image": "cat_dog_example.jpg",
    "text": "Represent the given image with the following question: What is in the image",
}])
text_embeddings = model.encode([
    {"text": "A cat and a dog"},
    {"text": "A cat and a tiger"},
])

similarity = model.similarity(image_embeddings, text_embeddings)
print(similarity)
# tensor([[0.3967, 0.3090]])
# ✅ The first text is most similar to the image

# Text -> Image
image_embeddings = model.encode([
    {"image": dog_cat_image_bytes, "text": "Represent the given image."},
])
text_embeddings = model.encode([
    {"text": "Find me an everyday image that matches the given caption: A cat and a dog."},
    {"text": "Find me an everyday image that matches the given caption: A cat and a tiger."},
])

similarity = model.similarity(image_embeddings, text_embeddings)
print(similarity)
# tensor([[0.4250, 0.3896]])
# ✅ The first text is most similar to the image
```

## Citation
```
@article{chen2025mmE5,
  title={mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data},
  author={Chen, Haonan and Wang, Liang and Yang, Nan and Zhu, Yutao and Zhao, Ziliang and Wei, Furu and Dou, Zhicheng},
  journal={arXiv preprint arXiv:2502.08468},
  year={2025}
}
```