| | --- |
| | license: apache-2.0 |
| | language: |
| | - en |
| | - it |
| | - fr |
| | - de |
| | - es |
| | base_model: |
| | - MrLight/dse-qwen2-2b-mrl-v1 |
| | tags: |
| | - vidore |
| | --- |
| | |
| | # mcdse-2b-v1 |
| |
|
| |  |
| |
|
| | mcdse-2b-v1 is an experimental model designed for multilingual visual document retrieval. |
| |
|
| | This model allows you to embed page/slide screenshots and query them using natural language. Whether it's tables, graphs, charts, schemas, images, or text, mcdse-2b-v1 encodes everything into a single embedding vector, eliminating the need for traditional OCR, document layout analysis, reading order detection, chunking, table/formula extraction... |
| |
|
| | - **Understands 🇮🇹 Italian, 🇪🇸 Spanish, 🇬🇧 English, 🇫🇷 French and 🇩🇪 German** |
| |
|
| | - **Matryoshka Representation Learning:** shrink embeddings from 1536 to 256 dimensions while maintaining 95% of the quality. A 6x reduction with negligible impact on performance! |
| |
|
| | - **Top-tier Binarization**: 768-dimensional binary vectors retain 99% retrieval quality of the original 1536-dimensional float vectors. With binary vectors, you can encode **100 million multilingual pages in just 10GB**. |
| |
|
| | - **Fast vLLM inference:** run inference on vLLM and efficiently serve embeddings at scale, production ready. |
| |
|
| | For more information about this model or how it was trained, visit the [announcement blogpost](https://huggingface.co/blog/marco/announcing-mcdse-2b-v1). |
| |
|
| | ## Usage |
| |
|
| | **Initialize model and processor** |
| | ```python |
| | from transformers import AutoProcessor, Qwen2VLForConditionalGeneration |
| | from PIL import Image |
| | import torch |
| | import math |
| | |
| | model = Qwen2VLForConditionalGeneration.from_pretrained( |
| | 'marco/mcdse-2b-v1', |
| | attn_implementation="flash_attention_2", |
| | torch_dtype=torch.bfloat16, |
| | device_map="cuda:0" |
| | ).eval() |
| | |
| | min_pixels = 1 * 28 * 28 |
| | max_pixels = 960 * 28 * 28 |
| | |
| | processor = AutoProcessor.from_pretrained( |
| | 'marco/mcdse-2b-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): |
| | 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 h_bar, w_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): |
| | 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) |
| | ``` |
| |
|
| | ### vLLM |
| | This model supports vLLM, visit the [announcement blogpost](https://huggingface.co/blog/marco/announcing-mcdse-2b-v1#deployment) to know more. |
| |
|
| | ## Results |
| | Given the scarcity of publicly available datasets for multilingual document image retrieval, the model has been evaluated using a custom-built dataset. This eval dataset was specifically designed to benchmark the model's performance across various languages. |
| |
|
| | ### NDCG@5 (float) |
| | | | Average | English | Italian | Spanish | French | German | |
| | |---------------------|------------|------------|------------|------------|------------|------------| |
| | | **1536 dimensions** | | | | | | | |
| | | dse-qwen2-2b-mrl-v1 | 79.5 | 79.2 | 80.2 | 77.9 | 80.6 | 79.6 | |
| | | mcdse-2b-v1 | **82.2** | **80.8** | **81.2** | **80.7** | **84.5** | **83.8** | |
| | | | **+3.28%** | **+1.98%** | **+1.23%** | **+3.47%** | **+4.62%** | **+5.01%** | |
| | | **1024 dimensions** | | | | | | | |
| | | dse-qwen2-2b-mrl-v1 | 78.3 | 78.8 | 78.5 | 76.5 | 80 | 77.5 | |
| | | mcdse-2b-v1 | **81.7** | **80** | **80.2** | **80.1** | **84** | **84.3** | |
| | | | **+4.23%** | **+1.75%** | **+2.12%** | **+4.49%** | **+4.76%** | **+8.07%** | |
| | | **768 dimensions** | | | | | | | |
| | | dse-qwen2-2b-mrl-v1 | 77.8 | 78.4 | 78.3 | 75.6 | 80.8 | 75.9 | |
| | | mcdse-2b-v1 | **81.1** | **79.6** | **79.9** | **79.2** | **83.3** | **83.3** | |
| | | | **+4.02%** | **+1.51%** | **+2.00%** | **+4.55%** | **+3.00%** | **+8.88%** | |
| | | **512 dimensions** | | | | | | | |
| | | dse-qwen2-2b-mrl-v1 | 76.2 | 77.6 | 75.9 | 73.1 | 79.2 | 75.2 | |
| | | mcdse-2b-v1 | **79.3** | **78.5** | **79.1** | **75.8** | **81.4** | **81.7** | |
| | | | **+3.91%** | **+1.15%** | **+4.05%** | **+3.56%** | **+2.70%** | **+7.96%** | |
| | | **384 dimensions** | | | | | | | |
| | | dse-qwen2-2b-mrl-v1 | 75.7 | 76.2 | 75.5 | 74.6 | 78.4 | 74 | |
| | | mcdse-2b-v1 | **78.8** | **77.5** | **78.5** | **76.1** | **80.4** | **81.4** | |
| | | | **+3.86%** | **+1.68%** | **+3.82%** | **+1.97%** | **+2.49%** | **+9.09%** | |
| | | **256 dimensions** | | | | | | | |
| | | dse-qwen2-2b-mrl-v1 | 73.5 | 74.5 | 73.6 | 70.6 | 74.8 | 73.8 | |
| | | mcdse-2b-v1 | **78.1** | **78.5** | **77.6** | **76.2** | **80.1** | **77.9** | |
| | | | **+5.89%** | **+5.10%** | **+5.15%** | **+7.35%** | **+6.62%** | **+5.26%** | |
| |
|
| | ### NDCG@5 (binary) |
| | | | Average | English | Italian | Spanish | French | German | |
| | |---------------------|-------------|-------------|-------------|-------------|-------------|-------------| |
| | | **1536 dimensions** | | | | | | | |
| | | dse-qwen2-2b-mrl-v1 | 75.0 | 75.8 | 75.4 | 72.4 | 78.1 | 73.2 | |
| | | mcdse-2b-v1 | **80.6** | **79.5** | **76.9** | **81.9** | **83.7** | **80.8** | |
| | | | **+6.93%** | **+4.65%** | **+1.95%** | **+11.60%** | **+6.69%** | **+9.41%** | |
| | | **1024 dimensions** | | | | | | | |
| | | dse-qwen2-2b-mrl-v1 | 72.2 | 74.8 | 71 | 70.8 | 74.6 | 69.6 | |
| | | mcdse-2b-v1 | **79.3** | **78.4** | **75.4** | **80.8** | **82.6** | **79.5** | |
| | | | **+9.05%** | **+4.59%** | **+5.84%** | **+12.38%** | **+9.69%** | **+12.45%** | |
| | | **768 dimensions** | | | | | | | |
| | | dse-qwen2-2b-mrl-v1 | 70.1 | 71.7 | 69.3 | 69.8 | 73.7 | 65.9 | |
| | | mcdse-2b-v1 | **78.8** | **77.1** | **75.4** | **80** | **83** | **78.5** | |
| | | | **+11.07%** | **+7.00%** | **+8.09%** | **+12.75%** | **+11.20%** | **+16.05%** | |
| | | **512 dimensions** | | | | | | | |
| | | dse-qwen2-2b-mrl-v1 | 66.5 | 70 | 65.4 | 63.7 | 70.2 | 63 | |
| | | mcdse-2b-v1 | **76.6** | **74.8** | **74.2** | **77.7** | **80.9** | **75.3** | |
| | | | **+13.21%** | **+6.42%** | **+11.86%** | **+18.02%** | **+13.23%** | **+16.33%** | |
| | | **384 dimensions** | | | | | | | |
| | | dse-qwen2-2b-mrl-v1 | 61.1 | 62.7 | 58.5 | 58.6 | 65.1 | 60.8 | |
| | | mcdse-2b-v1 | **74.3** | **74.5** | **71.4** | **77.2** | **75.2** | **73** | |
| | | | **+17.67%** | **+15.84%** | **+18.07%** | **+24.09%** | **+13.43%** | **+16.71%** | |
| | | **256 dimensions** | | | | | | | |
| | | dse-qwen2-2b-mrl-v1 | 54.3 | 59 | 56.5 | 53.6 | 53 | 49.6 | |
| | | mcdse-2b-v1 | **70.9** | **72.6** | **66.4** | **73.5** | **72.6** | **69.2** | |
| | | | **+23.31%** | **+18.73%** | **+14.91%** | **+27.07%** | **+27.00%** | **+28.32%** | |
