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*This model was contributed to Hugging Face Transformers on 2026-03-20.*

# 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`].

<hfoptions id="usage">
<hfoption id="Pipeline">

```python
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"])
```

</hfoption>

<hfoption id="AutoModel">

```python
from transformers import AutoModelForImageTextToText, AutoProcessor


model_path = "PaddlePaddle/PP-Chart2Table_safetensors"
model = AutoModelForImageTextToText.from_pretrained(
    model_path,
    device_map="auto",
)
processor = AutoProcessor.from_pretrained(model_path)

# 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",
            },
        ],
    },
]

inputs = processor.apply_chat_template(
    conversation,
    tokenize=True,
    add_generation_prompt=True,
    truncation=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=256)
generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
result = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(result)
```

</hfoption>
</hfoptions>

### Batched inference

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

<hfoptions id="usage">
<hfoption id="Pipeline">

```python
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"])
```

</hfoption>

<hfoption id="AutoModel">

```python
from transformers import AutoModelForImageTextToText, AutoProcessor


model_path = "PaddlePaddle/PP-Chart2Table_safetensors"
model = AutoModelForImageTextToText.from_pretrained(
    model_path,
    device_map="auto",
)
processor = AutoProcessor.from_pretrained(model_path)

# 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",
            },
        ],
    },
]

batch_conversation = [conversation, conversation]
inputs = processor.apply_chat_template(
    batch_conversation,
    tokenize=True,
    add_generation_prompt=True,
    truncation=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=256)
generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
result = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(result)
```

</hfoption>
</hfoptions>


## PPChart2TableConfig

[[autodoc]] PPChart2TableConfig

## PPChart2TableImageProcessor

[[autodoc]] PPChart2TableImageProcessor

## PPChart2TableImageProcessorPil

[[autodoc]] PPChart2TableImageProcessorPil

## PPChart2TableProcessor

[[autodoc]] PPChart2TableProcessor
