README.md

---
license: apache-2.0
language:
- en
- it
- fr
- de
- es
base_model:
- MrLight/dse-qwen2-2b-mrl-v1
tags:
- transformers
- sentence-transformers
- Qwen2-VL
datasets:
- llamaindex/vdr-multilingual-train
---

# vdr-2b-multi-v1

![](cover.png)

vdr-2b-multi-v1 is a multilingual embedding model designed for visual document retrieval across multiple languages and domains. It encodes document page screenshots into dense single-vector representations, this will effectively allow to search and query visually rich multilingual documents without the need for any OCR, data extraction pipelines, chunking...


- **Trained on 🇮🇹 Italian, 🇪🇸 Spanish, 🇬🇧 English, 🇫🇷 French and 🇩🇪 German:** together they form a new large, open-source, multilingual training dataset of 500k high-quality samples.

- **Cross-lingual Retrieval**: substantially better on real-world scenarios. For example, this allows for searching german documents with italian queries.

- **Matryoshka Representation Learning**: You can reduce the vectors size 3x and still keep 98% of the embeddings quality.

# Usage

The model uses bf16 tensors and allocates ~4.4GB of VRAM when loaded. You can easily run inference and generate embeddings using 768 image patches and a batch size of 16 even on a cheap NVIDIA T4 GPU. This table reports the memory footprint (GB) under conditions of different batch sizes with HuggingFace Transformers and maximum 768 image patches.

| Batch Size | GPU Memory (GB) |
|------------|-----------------|
|          4 |             6.9 |
|          8 |             8.8 |
|         16 |            11.5 |
|         32 |            19.7 |

You can generate embeddings with this model in many different ways:

<details open>
<summary>
via LlamaIndex
</summary>

```bash
pip install -U llama-index-embeddings-huggingface
```

```python
from llama_index.embeddings.huggingface import HuggingFaceEmbedding

model = HuggingFaceEmbedding(
    model_name="llamaindex/vdr-2b-multi-v1",
    device="cpu",  # "mps" for mac, "cuda" for nvidia GPUs
    trust_remote_code=True,
)

image_embedding = model.get_image_embedding("image.png")
query_embedding = model.get_query_embedding("some query")
```

</details>

<details>
<summary>
via HuggingFace Transformers
</summary>

```python
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
from PIL import Image
import torch
import math

# more pixels -> better embeddings -> more VRAM -> slower inference
# From my experience, 768 image patches is the right spot for compute efficient embeddings.
max_pixels = 768 * 28 * 28
min_pixels = 1 * 28 * 28

# Load the embedding model and processor
model = Qwen2VLForConditionalGeneration.from_pretrained(
    'llamaindex/vdr-2b-multi-v1',
    # These are the recommended kwargs for the model, but change them as needed
    attn_implementation="flash_attention_2",
    torch_dtype=torch.bfloat16,
    device_map="cuda:0"
).eval()

processor = AutoProcessor.from_pretrained(
    'llamaindex/vdr-2b-multi-v1',
    min_pixels=min_pixels,
    max_pixels=max_pixels
)

model.padding_side = "left"
processor.tokenizer.padding_side = "left"

document_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>What is shown in this image?<|im_end|>\n<|endoftext|>"

query_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Query: %s<|im_end|>\n<|endoftext|>"
```

**Encode queries**

```python
def encode_queries(queries: list[str], dimension: int) -> torch.Tensor:
    """
    Encode a list of queries into a tensor of embeddings.

    Args:
        queries: A list of strings, each representing a query.
        dimension: The desired dimension of the output embeddings.

    Returns:
        A tensor of shape (num_queries, dimension) containing the encoded queries.
    """

    dummy_image = Image.new('RGB', (56, 56))
    inputs = processor(
        text=[query_prompt % x for x in queries],
        images=[dummy_image for _ in queries],
        videos=None,
        padding='longest',
        return_tensors='pt'
    ).to('cuda:0')

    cache_position = torch.arange(0, len(queries))
    inputs = model.prepare_inputs_for_generation(
        **inputs, cache_position=cache_position, use_cache=False)

    with torch.no_grad():
        output = self.model(
            **inputs,
            return_dict=True,
            output_hidden_states=True
        )

    embeddings = output.hidden_states[-1][:, -1]
    return torch.nn.functional.normalize(embeddings[:, :dimension], p=2, dim=-1)
```

**Encode documents**

```python
def round_by_factor(number: float, factor: int) -> int:
    return round(number / factor) * factor

def ceil_by_factor(number: float, factor: int) -> int:
    return math.ceil(number / factor) * factor

def floor_by_factor(number: float, factor: int) -> int:
    return math.floor(number / factor) * factor

def smart_resize(height: int, width: int) -> tuple[int, int]:
    h_bar = max(28, round_by_factor(height, 28))
    w_bar = max(28, round_by_factor(width, 28))
    if h_bar * w_bar > max_pixels:
        beta = math.sqrt((height * width) / max_pixels)
        h_bar = floor_by_factor(height / beta, 28)
        w_bar = floor_by_factor(width / beta, 28)
    elif h_bar * w_bar < min_pixels:
        beta = math.sqrt(min_pixels / (height * width))
        h_bar = ceil_by_factor(height * beta, 28)
        w_bar = ceil_by_factor(width * beta, 28)
    return w_bar, h_bar

def resize(image: Image.Image):
    new_size = smart_resize(image.height, image.width)
    return image.resize(new_size)

def encode_documents(documents: list[Image.Image], dimension: int):
    """
    Encode a list of images into a tensor of embeddings.

    Args:
        documents: A list of PIL Image objects.
        dimension: The desired dimension of the output embeddings.

    Returns:
        A tensor of shape (num_documents, dimension) containing the encoded images.
    """
    
    inputs = processor(
        text=[document_prompt] * len(documents),
        images=[resize(x) for x in documents],
        videos=None,
        padding='longest',
        return_tensors='pt'
    ).to('cuda:0')

    cache_position = torch.arange(0, len(queries))
    inputs = model.prepare_inputs_for_generation(
        **inputs, cache_position=cache_position, use_cache=False)

    with torch.no_grad():
        output = self.model(
            **inputs,
            return_dict=True,
            output_hidden_states=True
        )
    
    embeddings = output.hidden_states[-1][:, -1]
    return torch.nn.functional.normalize(embeddings[:, :dimension], p=2, dim=-1)
```

</details>


<details>
<summary>
via SentenceTransformers
</summary>

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer(
    model_name_or_path="llamaindex/vdr-2b-multi-v1",
    device="cuda",
    trust_remote_code=True,
    # These are the recommended kwargs for the model, but change them as needed if you don't have CUDA
    model_kwargs={
        "torch_dtype": torch.bfloat16, 
        "device_map": "cuda:0", 
        "attn_implementation": "flash_attention_2"
    },
)

embeddings = model.encode("image.png")
```

</details>

# Training

The model is based on [MrLight/dse-qwen2-2b-mrl-v1](https://huggingface.co./MrLight/dse-qwen2-2b-mrl-v1) and it was trained on the new [vdr-multilingual-train](https://huggingface.co./datasets/llamaindex/vdr-multilingual-train) dataset that consinsists of 500k high quality, multilingual query image pairs. It was trained for 1 epoch using the [DSE approach](https://arxiv.org/abs/2406.11251), with a batch size of 128 and hard-mined negatives.

# Results

![](ndcgtop.png)

The model has been evaluated on the Vidore benchmark and on custom-built evaluation sets that allow testing its multilingual capabilities on text-only, visual-only and mixed page screenshots. The evaluation dataset is publicly available [here on HuggingFace](https://huggingface.co./datasets/llamaindex/vdr-multilingual-test).

All evaluations are performed by calculating **NDCG@5** scores using **1536 dimensions** vectors and an image resolution that can be represented with **maximum 768 tokens**.

|                     | Avg      | Italian (text) | Italian (visual) | Italian (mix) |
|---------------------|----------|----------------|------------------|---------------|
| dse-qwen2-2b-mrl-v1 |     95.1 |           95.1 |               94 |          96.2 |
| vdr-2b-multi-v1     | **97.0** |       **96.4** |         **96.3** |      **98.4** |
|                     |  **+2%** |                |                  |               |

|                     | Avg       | French (text) | French (visual) | French (mix) |
|---------------------|-----------|---------------|-----------------|--------------|
| dse-qwen2-2b-mrl-v1 |      93.5 |          94.7 |            90.8 |         95.1 |
| vdr-2b-multi-v1     |  **95.6** |      **95.6** |        **93.3** |     **97.9** |
|                     | **+2.2%** |               |                 |              |

|                     | Avg       | Spanish (text) | Spanish (visual) | Spanish (mix) |
|---------------------|-----------|----------------|------------------|---------------|
| dse-qwen2-2b-mrl-v1 |      96.7 |           97.2 |             94.7 |          98.2 |
| vdr-2b-multi-v1     |  **98.1** |       **98.3** |         **96.9** |      **99.1** |
|                     | **+1.4%** |                |                  |               |

|                     | Avg       | German (text) | German (visual) | German (mix) |
|---------------------|-----------|---------------|-----------------|--------------|
| dse-qwen2-2b-mrl-v1 |      93.0 |          93.4 |              90 |         95.5 |
| vdr-2b-multi-v1     |  **96.2** |      **94.8** |        **95.7** |     **98.1** |
|                     | **+3.4%** |               |                 |              |

|                     | Avg       | English (text) | English (visual) | English (mix) |
|---------------------|-----------|----------------|------------------|---------------|
| dse-qwen2-2b-mrl-v1 | 98.0      | **98.3**       | 98.5             | 97.1          |
| vdr-2b-multi-v1     | **98.1**  | 97.9           | **99.1**         | **97.3**      |
|                     | **+0.1%** |                |                  |               |

|                     |  **Avg** | **shiftproject** | **government** | **healthcare** | **energy** | **ai**     | **docvqa** | **arxivqa** | **tatdqa** | **infovqa** | **tabfquad** |
|--------------------:|---------:|-----------------:|---------------:|---------------:|-----------:|-----------:|-----------:|------------:|-----------:|------------:|-------------:|
| dse-qwen2-2b-mrl-v1 |     83.6 |             79.8 |       **95.7** |       **96.9** |     **92** |   98.2     |       56.3 |    **85.2** |   **53.9** |    **87.5** |         90.3 |
|     vdr-2b-multi-v1 | **84.0** |         **82.4** |           95.5 |           96.5 |       91.2 |   **98.5** |   **58.5** |        84.7 |       53.6 |        87.1 |     **92.2** |