|
|
--- |
|
|
language: |
|
|
- en |
|
|
- fr |
|
|
- ro |
|
|
- de |
|
|
- multilingual |
|
|
pipeline_tag: image-to-text |
|
|
tags: |
|
|
- image-captioning |
|
|
license: apache-2.0 |
|
|
--- |
|
|
# Model card for DePlot |
|
|
|
|
|
 |
|
|
|
|
|
|
|
|
# Table of Contents |
|
|
|
|
|
0. [TL;DR](#TL;DR) |
|
|
1. [Using the model](#using-the-model) |
|
|
2. [Contribution](#contribution) |
|
|
3. [Citation](#citation) |
|
|
|
|
|
# TL;DR |
|
|
|
|
|
The abstract of the paper states that: |
|
|
|
|
|
> Visual language such as charts and plots is ubiquitous in the human world. Comprehending plots and charts requires strong reasoning skills. Prior state-of-the-art (SOTA) models require at least tens of thousands of training examples and their reasoning capabilities are still much limited, especially on complex human-written queries. This paper presents the first one-shot solution to visual language reasoning. We decompose the challenge of visual language reasoning into two steps: (1) plot-to-text translation, and (2) reasoning over the translated text. The key in this method is a modality conversion module, named as DePlot, which translates the image of a plot or chart to a linearized table. The output of DePlot can then be directly used to prompt a pretrained large language model (LLM), exploiting the few-shot reasoning capabilities of LLMs. To obtain DePlot, we standardize the plot-to-table task by establishing unified task formats and metrics, and train DePlot end-to-end on this task. DePlot can then be used off-the-shelf together with LLMs in a plug-and-play fashion. Compared with a SOTA model finetuned on more than >28k data points, DePlot+LLM with just one-shot prompting achieves a 24.0% improvement over finetuned SOTA on human-written queries from the task of chart QA. |
|
|
|
|
|
|
|
|
# Using the model |
|
|
|
|
|
## Converting from T5x to huggingface |
|
|
|
|
|
You can use the [`convert_pix2struct_checkpoint_to_pytorch.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/convert_pix2struct_original_pytorch_to_hf.py) script as follows: |
|
|
```bash |
|
|
python convert_pix2struct_checkpoint_to_pytorch.py --t5x_checkpoint_path PATH_TO_T5X_CHECKPOINTS --pytorch_dump_path PATH_TO_SAVE --is_vqa |
|
|
``` |
|
|
if you are converting a large model, run: |
|
|
```bash |
|
|
python convert_pix2struct_checkpoint_to_pytorch.py --t5x_checkpoint_path PATH_TO_T5X_CHECKPOINTS --pytorch_dump_path PATH_TO_SAVE --use-large --is_vqa |
|
|
``` |
|
|
Once saved, you can push your converted model with the following snippet: |
|
|
```python |
|
|
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor |
|
|
|
|
|
model = Pix2StructForConditionalGeneration.from_pretrained(PATH_TO_SAVE) |
|
|
processor = Pix2StructProcessor.from_pretrained(PATH_TO_SAVE) |
|
|
|
|
|
model.push_to_hub("USERNAME/MODEL_NAME") |
|
|
processor.push_to_hub("USERNAME/MODEL_NAME") |
|
|
``` |
|
|
|
|
|
## Run a prediction |
|
|
|
|
|
You can run a prediction by querying an input image together with a question as follows: |
|
|
```python |
|
|
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor |
|
|
import requests |
|
|
from PIL import Image |
|
|
|
|
|
model = Pix2StructForConditionalGeneration.from_pretrained('google/deplot') |
|
|
processor = Pix2StructProcessor.from_pretrained('google/deplot') |
|
|
url = "https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/val/png/5090.png" |
|
|
image = Image.open(requests.get(url, stream=True).raw) |
|
|
|
|
|
inputs = processor(images=image, text="Generate underlying data table of the figure below:", return_tensors="pt") |
|
|
predictions = model.generate(**inputs, max_new_tokens=512) |
|
|
print(processor.decode(predictions[0], skip_special_tokens=True)) |
|
|
``` |
|
|
|
|
|
# Contribution |
|
|
|
|
|
This model was originally contributed by Fangyu Liu, Julian Martin Eisenschlos et al. and added to the Hugging Face ecosystem by [Younes Belkada](https://huggingface.co/ybelkada). |
|
|
|
|
|
# Citation |
|
|
|
|
|
If you want to cite this work, please consider citing the original paper: |
|
|
``` |
|
|
@misc{liu2022matcha, |
|
|
title={MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering}, |
|
|
author={Fangyu Liu and Francesco Piccinno and Syrine Krichene and Chenxi Pang and Kenton Lee and Mandar Joshi and Yasemin Altun and Nigel Collier and Julian Martin Eisenschlos}, |
|
|
year={2022}, |
|
|
eprint={2212.09662}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CL} |
|
|
} |
|
|
``` |
