metadata

language:
  - en
tags:
  - information retrieval
  - embedding model
  - visual information retrieval
metrics:
  - recall
pipeline_tag: feature-extraction
license: apache-2.0

OCR-free Visual Document Embedding Model as Your Personal Librarian

The model only takes images as document-side inputs and produce vectors representing document pages. minicpm-visual-embedding-v0 is trained with over 200k query-visual document pairs, including textual document, visual document, arxiv figures, industry documents, textbooks, ebooks, etc. The performance of minicpm-visual-embedding-v0 is on a par with our ablation text embedding model on text-oriented documents, and an advantages on visually-intensive documents.

News

2024-07-14: We released a Gradio demo of miniCPM-visual-embedding-v0, take a look at pipeline_gradio.py. We consider hosting a huggingface space to deploy this.
2024-07-13: We released a command-line based demo of miniCPM-visual-embedding-v0 for users to retireve most relavant pages from a given PDF file (could be very long), take a look at pipeline.py.
2024-06-27: 🚀 We released our first visual embedding model checkpoint minicpm-visual-embedding-v0 on huggingface.
2024-05-08: 🌍 We open-sourced our training code (full-parameter tuning with GradCache and DeepSpeed, supports large batch size across multiple GPUs with zero-stage1) and eval code.

Get started

Pip install all dependencies:

Pillow==10.1.0
timm==0.9.10
torch==2.1.2
torchvision==0.16.2
transformers==4.36.0
sentencepiece==0.1.99
numpy==1.26.0

First you are suggested to git clone this huggingface repo or download repo with huggingface_cli.

git lfs install
git clone https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0

huggingface-cli download --resume-download RhapsodyAI/minicpm-visual-embedding-v0 --local-dir minicpm-visual-embedding-v0 --local-dir-use-symlinks False

from transformers import AutoModel
from transformers import AutoTokenizer
from PIL import Image
import torch

device = 'cuda:0'

# Load model, be sure to substitute `model_path` by your model path 
model_path = '/local/path/to/model'
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
model.to(device)

# Load image to PIL.Image object
image_1 = Image.open('/local/path/to/images/memex.png').convert('RGB')
image_2 = Image.open('/local/path/to/images/us2020.png').convert('RGB')
image_3 = Image.open('/local/path/to/images/hard_negative.png').convert('RGB')

# User query
query_instruction = 'Represent this query for retrieving relavant document: '
query = 'Who was elected as president of United States in 2020?'
query_full = query_instruction + query

# Embed image documents
with torch.no_grad():
    p_reps = model(text=['', '', ''], image=[image_1, image_2, image_3], tokenizer=tokenizer).reps

# Embed text queries
with torch.no_grad():
    q_reps = model(text=[query_full], image=[None], tokenizer=tokenizer).reps # [B, s, d]

# Calculate similarities
scores = torch.matmul(q_reps, p_reps.T)
print(scores)
# tensor([[-0.0112,  0.3316,  0.2376]], device='cuda:0')

Limitations

This checkpoint is an alpha version, and may not be strong in your tasks, for bad case, please create an issue to let us know, many thanks!
The modeling script modeling_minicpmv on huggingface is not standard yet, the inference code could be further improved.
The inference speed is low, because vision encoder uses timm, which does not yet support flash-attn.

Citation

If you find our work useful, please consider cite us:

@misc{RhapsodyEmbedding2024,
  author = {RhapsodyAI},
  title = {OCR-free Visual Document Embedding Model as Your Personal Librarian},
  year = {2024},
  howpublished = {\url{https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0}},
  note = {Accessed: 2024-06-28}
}