README.md

---
language: en
---

# ChartVE (Chart Visual Entailment)

ChartVE is a visual entailment model introduced in the paper "Do LVLMs Understand Charts?  
Analyzing and Correcting Factual Errors in Chart Captioning" for evaluating the factuality of a generated caption sentence with regard to the input chart. The model takes in a chart figure and a caption sentence as input, and outputs an entailment probability.  To compute the the entailment probability, please refer to the "How to use" section below.  The underlying architecture of this model is UniChart.

Note that this model expects a caption sentence as textual inputs. For captions that are longer than one sentences, one should split the caption into multiple sentences, feed individual sentences to ChartVE, and then aggregate the scores. Below, we provide an example of how to use ChartVE.


### How to use

Using the pre-trained model directly:
```python
from transformers import DonutProcessor, VisionEncoderDecoderModel
from PIL import Image

model_name = "khhuang/chartve"
model = VisionEncoderDecoderModel.from_pretrained(model_name).cuda()
processor = DonutProcessor.from_pretrained(model_name)

image_path = "PATH_TO_IMAGE"

def format_query(sentence):
    return f"Does the image entails this statement: \"{sentence}\"?"

# Format text inputs
CAPTION_SENTENCE = "The state that has the highest number of population is California."
query = format_query(CAPTION_SENTENCE)

# Encode chart figure and tokenize text
img = Image.open(IMAGE_PATH)
pixel_values = processor(img.convert("RGB"), random_padding=False, return_tensors="pt").pixel_values
pixel_values = pixel_values.cuda()
decoder_input_ids = processor.tokenizer(query, add_special_tokens=False, return_tensors="pt", max_length=510).input_ids.cuda()#.squeeze(0)


outputs = model(pixel_values, decoder_input_ids=decoder_input_ids)

# positive_logit = outputs['logits'].squeeze()[-1,49922]
# negative_logit = outputs['logits'].squeeze()[-1,2334] 

# Probe the probability of generating "yes"
binary_entail_prob_positive = torch.nn.functional.softmax(outputs['logits'].squeeze()[-1,[2334, 49922]])[1].item()

# binary_entail_prob_positive corresponds to the computed probability that the chart entails the caption sentence.
```

### Citation
```
@misc{huang-etal-2023-do,
    title = "Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning",
    author = "Huang, Kung-Hsiang  and
      Zhou, Mingyang and
      Chan, Hou Pong  and
      Fung, Yi R. and
      Wang, Zhenhailong and
      Zhang, Lingyu and
      Chang, Shih-Fu and
      Ji, Heng",
    year={2023},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}   
```