This model was released on 2025-05-20 and added to Hugging Face Transformers on 2026-03-18.

PP-Chart2Table

Overview

PP-Chart2Table is a SOTA multimodal model developed by the PaddlePaddle team, specializing in chart parsing for both Chinese and English. Its high performance is driven by a novel “Shuffled Chart Data Retrieval” training task, which, combined with a refined token masking strategy, significantly improves its efficiency in converting charts to data tables. The model is further strengthened by an advanced data synthesis pipeline that uses high-quality seed data, RAG, and LLMs persona design to create a richer, more diverse training set. To address the challenge of large-scale unlabeled, out-of-distribution (OOD) data, the team implemented a two-stage distillation process, ensuring robust adaptability and generalization on real-world data.

Model Architecture

PP-Chart2Table adopts a multimodal fusion architecture that combines a vision tower for chart feature extraction and a language model for table structure generation, enabling end-to-end chart-to-table conversion.

Usage

Single input inference

The example below demonstrates how to classify image with PP-Chart2Table using Pipeline or the AutoModel.

from transformers import pipeline

pipe = pipeline("image-text-to-text", model="PaddlePaddle/PP-Chart2Table_safetensors")

# PPChart2TableProcessor uses hardcoded "Chart to table" instruction internally via chat template
conversation = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "url": "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/chart_parsing_02.png",
            },
        ],
    },
]
result = pipe(text=conversation)
print(result[0]["generated_text"])

Batched inference

Here is how you can do it with PP-Chart2Table using Pipeline or the AutoModel:

from transformers import pipeline

pipe = pipeline("image-text-to-text", model="PaddlePaddle/PP-Chart2Table_safetensors")

# PPChart2TableProcessor uses hardcoded "Chart to table" instruction internally via chat template
conversation = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "url": "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/chart_parsing_02.png",
            },
        ],
    },
]
result = pipe(text=[conversation, conversation])
print(result[0][0]["generated_text"])

PPChart2TableConfig

>>> from transformers import GotOcr2ForConditionalGeneration, PPChart2TableConfig

>>> # Initializing a PPChart2Table style configuration
>>> configuration = PPChart2TableConfig()

>>> # Initializing a model from the PaddlePaddle/PP-Chart2Table_safetensors style configuration
>>> model = GotOcr2ForConditionalGeneration(configuration)  # underlying architecture is Got Ocr 2

>>> # Accessing the model configuration
>>> configuration = model.config

PPChart2TableImageProcessor

PPChart2TableImageProcessorPil

PPChart2TableProcessor