language:
- en
tags:
- information retrieval
- embedding model
- visual information retrieval
metrics:
- recall
pipeline_tag: feature-extraction
OCR-free Visual Document Embedding Model as Your Personal Librarian
With MiniCPM-Visual-Embedding, it is possible to directly build knowledge base with raw PDF/Book/Document without any OCR technique nor OCR pipeline. The model only takes images as document-side inputs and produce vectors representing document pages. minicpm-visual-embedding-v0 is trained with over 30k paired query - visual document pages, including textual document, visual document, arxiv figures, industry documents, textbooks, ebooks, etc. The performance of minicpm-visual-embedding-v0 is on a par with a text embedding on text-oriented documents, and an advantages on visually-intensive documents.
News
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
or
huggingface-cli download RhapsodyAI/minicpm-visual-embedding-v0
from transformers import AutoModel
from transformers import AutoTokenizer
from PIL import Image
import torch
device = 'cuda:0'
# This function is borrowed from https://huggingface.co/intfloat/e5-mistral-7b-instruct
def last_token_pool(last_hidden_states, attention_mask):
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
# 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_outputs = model(text=['', '', ''], image=[image_1, image_2, image_3], tokenizer=tokenizer)
p_reps = last_token_pool(p_outputs.last_hidden_state, p_outputs.attention_mask)
# Embed text queries
with torch.no_grad():
q_outputs = model(text=[query_full], image=[None], tokenizer=tokenizer) # [B, s, d]
q_reps = last_token_pool(q_outputs.last_hidden_state, q_outputs.attention_mask) # [B, d]
# Calculate similarities
scores = torch.matmul(q_reps, p_reps.T)
print(scores)
# tensor([[0.6506, 4.9630, 3.8614]], device='cuda:0')
Limitations
Currently, please ensure that image sizes within the same knowledge base be similar. High variance of image size may cause the model performance degrade. We will augment data and fix this issue in our future version.
Citation
If you find our work useful, please consider cite us:
@misc{RhapsodyAI2024,
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}
}