PP-DocBee-7B
Introduction
The PaddleOCR team has developed PP-DocBee-7B, a multimodal large model focusing on document understanding, and it performs excellently in Chinese document understanding tasks. The model is fine-tuned and optimized using nearly 5 million multimodal datasets for document understanding, including general VQA, OCR, charts, text-rich documents, mathematics and complex reasoning, synthetic data, and pure text data, with different training data ratios set. On several authoritative English document understanding evaluation lists in academia, PP-DocBee has basically achieved SOTA for models of the same parameter scale. In terms of internal business Chinese scenario indicators, PP-DocBee also outperforms the current popular open-source and closed-source models. The key accuracy metrics are as follow:
| Model | Model Storage Size(GB) | Total Score |
|---|---|---|
| PP-DocBee-2B | 4.2 | 765 |
| PP-DocBee-7B | 15.8 | - |
Note: The total scores of the above models are test results from an internal evaluation set, where all images have a resolution (height, width) of (1680, 1204), with a total of 1196 data entries, covering scenarios such as financial reports, laws and regulations, scientific and technical papers, manuals, humanities papers, contracts, research reports, etc. There are no plans for public release at the moment.
Quick Start
Installation
- PaddlePaddle
Please refer to the following commands to install PaddlePaddle using pip:
# for CUDA11.8
python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
# for CUDA12.6
python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
# for CPU
python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
For details about PaddlePaddle installation, please refer to the PaddlePaddle official website.
- PaddleOCR
Install the latest version of the PaddleOCR inference package from PyPI:
python -m pip install paddleocr
Model Usage
You can quickly experience the functionality with a single command:
paddleocr doc_vlm \
--model_name PP-DocBee-7B \
-i "{'image': 'https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png', 'query': 'Recognize the content of this table and output it in markdown format.'}"
You can also integrate the model inference of the document visual-language module into your project. Before running the following code, please download the sample image to your local machine.
from paddleocr import DocVLM
model = DocVLM(model_name="PP-DocBee-7B")
results = model.predict(
input={
"image": "https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png",
"query": "Recognize the content of this table and output it in markdown format."
},
batch_size=1
)
for res in results:
res.print()
res.save_to_json(f"./output/res.json")
After running, the obtained result is as follows:
{'res': {'image': 'medal_table_en.png', 'query': 'Recognize the content of this table and output it in markdown format', 'result': '| Rank | Country/Region | Gold | Silver | Bronze | Total Medals |\n|---|---|---|---|---|---|\n| 1 | China (CHN) | 48 | 22 | 30 | 100 |\n| 2 | United States (USA) | 36 | 39 | 37 | 112 |\n| 3 | Russia (RUS) | 24 | 13 | 23 | 60 |\n| 4 | Great Britain (GBR) | 19 | 13 | 19 | 51 |\n| 5 | Germany (GER) | 16 | 11 | 14 | 41 |\n| 6 | Australia (AUS) | 14 | 15 | 17 | 46 |\n| 7 | South Korea (KOR) | 13 | 11 | 8 | 32 |\n| 8 | Japan (JPN) | 9 | 8 | 8 | 25 |\n| 9 | Italy (ITA) | 8 | 9 | 10 | 27 |\n| 10 | France (FRA) | 7 | 16 | 20 | 43 |\n| 11 | Netherlands (NED) | 7 | 5 | 4 | 16 |\n| 12 | Ukraine (UKR) | 7 | 4 | 11 | 22 |\n| 13 | Kenya (KEN) | 6 | 4 | 6 | 16 |\n| 14 | Spain (ESP) | 5 | 11 | 3 | 19 |\n| 15 | Jamaica (JAM) | 5 | 4 | 2 | 11 |\n'}}
The visualized result is as follows:
| Rank | Country/Region | Gold | Silver | Bronze | Total Medals |
|---|---|---|---|---|---|
| 1 | China (CHN) | 48 | 22 | 30 | 100 |
| 2 | United States (USA) | 36 | 39 | 37 | 112 |
| 3 | Russia (RUS) | 24 | 13 | 23 | 60 |
| 4 | Great Britain (GBR) | 19 | 13 | 19 | 51 |
| 5 | Germany (GER) | 16 | 11 | 14 | 41 |
| 6 | Australia (AUS) | 14 | 15 | 17 | 46 |
| 7 | South Korea (KOR) | 13 | 11 | 8 | 32 |
| 8 | Japan (JPN) | 9 | 8 | 8 | 25 |
| 9 | Italy (ITA) | 8 | 9 | 10 | 27 |
| 10 | France (FRA) | 7 | 16 | 20 | 43 |
| 11 | Netherlands (NED) | 7 | 5 | 4 | 16 |
| 12 | Ukraine (UKR) | 7 | 4 | 11 | 22 |
| 13 | Kenya (KEN) | 6 | 4 | 6 | 16 |
| 14 | Spain (ESP) | 5 | 11 | 3 | 19 |
| 15 | Jamaica (JAM) | 5 | 4 | 2 | 11 |
For details about usage command and descriptions of parameters, please refer to the Document.
Pipeline Usage
The ability of a single model is limited. But the pipeline consists of several models can provide more capacity to resolve difficult problems in real-world scenarios.
doc_understanding
The document understanding pipeline is an advanced document processing technology based on Visual-Language Models (VLM), designed to overcome the limitations of traditional document processing. And there is only 1 module in the pipeline:
- Document Visual Language Module
Run a single command to quickly experience the OCR pipeline:
paddleocr doc_understanding -i "{'image': 'https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png', 'query': 'Recognize the content of this table and output it in markdown format.'}"
Results are printed to the terminal:
{'res': {'image': 'medal_table_en.png', 'query': 'Recognize the content of this table and output it in markdown format', 'result': '| Rank | Country/Region | Gold | Silver | Bronze | Total Medals |\n|---|---|---|---|---|---|\n| 1 | China (CHN) | 48 | 22 | 30 | 100 |\n| 2 | United States (USA) | 36 | 39 | 37 | 112 |\n| 3 | Russia (RUS) | 24 | 13 | 23 | 60 |\n| 4 | Great Britain (GBR) | 19 | 13 | 19 | 51 |\n| 5 | Germany (GER) | 16 | 11 | 14 | 41 |\n| 6 | Australia (AUS) | 14 | 15 | 17 | 46 |\n| 7 | South Korea (KOR) | 13 | 11 | 8 | 32 |\n| 8 | Japan (JPN) | 9 | 8 | 8 | 25 |\n| 9 | Italy (ITA) | 8 | 9 | 10 | 27 |\n| 10 | France (FRA) | 7 | 16 | 20 | 43 |\n| 11 | Netherlands (NED) | 7 | 5 | 4 | 16 |\n| 12 | Ukraine (UKR) | 7 | 4 | 11 | 22 |\n| 13 | Kenya (KEN) | 6 | 4 | 6 | 16 |\n| 14 | Spain (ESP) | 5 | 11 | 3 | 19 |\n| 15 | Jamaica (JAM) | 5 | 4 | 2 | 11 |\n'}}
If save_path is specified, the visualization results will be saved under save_path. The visualization output is shown below:
The command-line method is for quick experience. For project integration, also only a few codes are needed as well:
from paddleocr import DocUnderstanding
pipeline = DocUnderstanding(
doc_understanding_model_name="PP-DocBee-7B"
)
output = pipeline.predict(
{
"image": "https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png",
"query": "Recognize the content of this table and output it in markdown format."
}
)
for res in output:
res.print() ## Print the structured output of the prediction
res.save_to_json("./output/")
The default model used in pipeline is PP-DocBee2-3B, so you need to specify doc_understanding_model_name to PP-DocBee-7B. And you can also use the local model file by argument doc_understanding_model_dir. For details about usage command and descriptions of parameters, please refer to the Document.
Links
- Downloads last month
- 0