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---
language:
- en
- fr
license: apache-2.0
library_name: Tevatron
tags:
- vidore
datasets:
- Tevatron/docmatix-ir
- HuggingFaceM4/Docmatix
- Tevatron/msmarco-passage-aug
- vidore/colpali_train_set
- Tevatron/wiki-ss-nq
base_model:
- Qwen/Qwen2-VL-2B-Instruct
pipeline_tag: visual-document-retrieval
---
# DSE-QWen2-2b-MRL-V1
DSE-QWen2-2b-MRL-V1 is a bi-encoder model designed to encode document screenshots into dense vectors for document retrieval.
The Document Screenshot Embedding ([DSE](https://arxiv.org/abs/2406.11251)) approach captures documents in their original visual format, preserving all information such as text, images, and layout, thus avoiding tedious parsing and potential information loss.
DSE aims to provide a generalizable embedding model for Text, PDF documents, Webpage, Slides retrieval.
For example, DSE-QWen2-2b-MRL-V1 achieves **85.8** nDCG@5 on [ViDoRE](https://huggingface.co/spaces/vidore/vidore-leaderboard) leaderboard.
## Note:
QWen vision encoder may take high GPU memory if the input image is large. Adjust `'resized_height':680 , 'resized_width':680` (see below) to fit VRAM based on GPU resources.
## How to Use the Model
To support better effectiveness--efficiency trade-off, this checkpoint is trained to support:
1. Flexible representation dimension.
2. Flexible input image size.
### Load the Model and Processor
```python
import torch
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
from qwen_vl_utils import process_vision_info
min_pixels = 1*28*28
max_pixels = 2560*28*28
processor = AutoProcessor.from_pretrained("MrLight/dse-qwen2-2b-mrl-v1", min_pixels=min_pixels, max_pixels=max_pixels)
model = Qwen2VLForConditionalGeneration.from_pretrained('MrLight/dse-qwen2-2b-mrl-v1', attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16).to('cuda:0').eval()
processor.tokenizer.padding_side = "left"
model.padding_side = "left"
def get_embedding(last_hidden_state: torch.Tensor, dimension: int) -> torch.Tensor:
reps = last_hidden_state[:, -1]
reps = torch.nn.functional.normalize(reps[:, :dimension], p=2, dim=-1)
return reps
```
### Encode Text Query
```python
from PIL import Image
queries = ["Where can we see Llama?", "What is the LLaMA AI model?"]
query_messages = []
for query in queries:
message = [
{
'role': 'user',
'content': [
{'type': 'image', 'image': Image.new('RGB', (28, 28)), 'resized_height':1 , 'resized_width':1}, # need a dummy image here for an easier process.
{'type': 'text', 'text': f'Query: {query}'},
]
}
]
query_messages.append(message)
query_texts = [
processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True) + "<|endoftext|>"
for msg in query_messages
]
query_image_inputs, query_video_inputs = process_vision_info(query_messages)
query_inputs = processor(text=query_texts, images=query_image_inputs, videos=query_video_inputs, padding='longest', return_tensors='pt').to('cuda:0')
cache_position = torch.arange(0, len(query_texts))
query_inputs = model.prepare_inputs_for_generation(**query_inputs, cache_position=cache_position, use_cache=False)
with torch.no_grad():
output = model(**query_inputs, return_dict=True, output_hidden_states=True)
query_embeddings = get_embedding(output.hidden_states[-1], 1536) # adjust dimensionality for efficiency trade-off, e.g. 512
```
### Encode Document Screenshot
```python
import requests
from io import BytesIO
# URLs of the images
url1 = "https://huggingface.co/Tevatron/dse-phi3-docmatix-v2/resolve/main/animal-llama.png"
url2 = "https://huggingface.co/Tevatron/dse-phi3-docmatix-v2/resolve/main/meta-llama.png"
# Download and open images
response1 = requests.get(url1)
response2 = requests.get(url2)
doc_image1 = Image.open(BytesIO(response1.content))
doc_image2 = Image.open(BytesIO(response2.content))
doc_images = [doc_image1, doc_image2]
doc_messages = []
for doc in doc_images:
message = [
{
'role': 'user',
'content': [
{'type': 'image', 'image': doc}, #'resized_height':680 , 'resized_width':680} # adjust the image size for efficiency trade-off
{'type': 'text', 'text': 'What is shown in this image?'}
]
}
]
doc_messages.append(message)
doc_texts = [
processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True) + "<|endoftext|>"
for msg in doc_messages
]
doc_image_inputs, doc_video_inputs = process_vision_info(doc_messages)
doc_inputs = processor(text=doc_texts, images=doc_image_inputs, videos=doc_video_inputs, padding='longest', return_tensors='pt').to('cuda:0')
cache_position = torch.arange(0, len(doc_texts))
doc_inputs = model.prepare_inputs_for_generation(**doc_inputs, cache_position=cache_position, use_cache=False)
with torch.no_grad():
output = model(**doc_inputs, return_dict=True, output_hidden_states=True)
doc_embeddings = get_embedding(output.hidden_states[-1], 1536) # adjust dimensionality for efficiency trade-off e.g. 512
```
### Compute Similarity
```python
from torch.nn.functional import cosine_similarity
num_queries = query_embeddings.size(0)
num_passages = doc_embeddings.size(0)
for i in range(num_queries):
query_embedding = query_embeddings[i].unsqueeze(0)
similarities = cosine_similarity(query_embedding, doc_embeddings)
print(f"Similarities for Query {i+1}: {similarities.cpu().float().numpy()}")
```
### Encode Document Text
This DSE checkpoint is warm-up with `Tevatron/msmarco-passage-aug`, thus the model can also effectively encode document as text input.
```python
doc_texts = [
"The llama (/ˈlɑːmə/; Spanish pronunciation: [ˈʎama] or [ˈʝama]) (Lama glama) is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era.",
"Llama (acronym for Large Language Model Meta AI, and formerly stylized as LLaMA) is a family of autoregressive large language models (LLMs) released by Meta AI starting in February 2023.[2][3] The latest version is Llama 3.1, released in July 2024.[4]"
]
doc_messages = []
for doc in doc_texts:
message = [
{
'role': 'user',
'content': [
{'type': 'image', 'image': Image.new('RGB', (28, 28)), 'resized_height':1 , 'resized_width':1}, # need a dummy image here for an easier process.
{'type': 'text', 'text': f'Document: {doc}'}
]
}
]
doc_messages.append(message)
doc_texts = [
processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True) + "<|endoftext|>"
for msg in doc_messages
]
doc_image_inputs, doc_video_inputs = process_vision_info(doc_messages)
doc_inputs = processor(text=doc_texts, images=doc_image_inputs, videos=doc_video_inputs, padding='longest', return_tensors='pt').to('cuda:0')
cache_position = torch.arange(0, len(doc_texts))
doc_inputs = model.prepare_inputs_for_generation(**doc_inputs, cache_position=cache_position, use_cache=False)
with torch.no_grad():
output = model(**doc_inputs, return_dict=True, output_hidden_states=True)
doc_embeddings = get_embedding(output.hidden_states[-1], 1536) # adjust dimensionality for efficiency trade-off e.g. 512
for i in range(num_queries):
query_embedding = query_embeddings[i].unsqueeze(0)
similarities = cosine_similarity(query_embedding, doc_embeddings)
print(f"Similarities for Query {i+1}: {similarities.cpu().float().numpy()}")
```
### Citation
If you find this checkpoint is helpful, please consider citing QWen2, Docmatix, ViDoRe, and our DSE work.

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{
"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
}

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{
"architectures": [
"Qwen2VLForConditionalGeneration"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151645,
"vision_start_token_id": 151652,
"vision_end_token_id": 151653,
"vision_token_id": 151654,
"image_token_id": 151655,
"video_token_id": 151656,
"hidden_act": "silu",
"hidden_size": 1536,
"initializer_range": 0.02,
"intermediate_size": 8960,
"max_position_embeddings": 32768,
"max_window_layers": 28,
"model_type": "qwen2_vl",
"num_attention_heads": 12,
"num_hidden_layers": 28,
"num_key_value_heads": 2,
"rms_norm_eps": 1e-06,
"rope_theta": 1000000.0,
"sliding_window": 32768,
"tie_word_embeddings": true,
"torch_dtype": "bfloat16",
"transformers_version": "4.41.2",
"use_cache": true,
"use_sliding_window": false,
"vision_config": {
"depth": 32,
"embed_dim": 1280,
"mlp_ratio": 4,
"num_heads": 16,
"in_chans": 3,
"hidden_size": 1536,
"patch_size": 14,
"spatial_merge_size": 2,
"spatial_patch_size": 14,
"temporal_patch_size": 2
},
"rope_scaling": {
"type": "mrope",
"mrope_section": [
16,
24,
24
]
},
"vocab_size": 151936
}

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{
"bos_token_id": 151643,
"pad_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"repetition_penalty": 1.0,
"temperature": 0.01,
"top_p": 0.001,
"top_k": 1,
"transformers_version": "4.37.0"
}

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{
"min_pixels": 3136,
"max_pixels": 12845056,
"patch_size": 14,
"temporal_patch_size": 2,
"merge_size": 2,
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"image_processor_type": "Qwen2VLImageProcessor",
"processor_class": "Qwen2VLProcessor"
}

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},
"151655": {
"content": "<|image_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151656": {
"content": "<|video_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"additional_special_tokens": ["<|im_start|>", "<|im_end|>", "<|object_ref_start|>","<|object_ref_end|>","<|box_start|>","<|box_end|>","<|quad_start|>","<|quad_end|>","<|vision_start|>","<|vision_end|>","<|vision_pad|>","<|image_pad|>","<|video_pad|>"],
"bos_token": null,
"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"padding_side": "left",
"errors": "replace",
"model_max_length": 32768,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

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