# **ko-deplot**

ko-deplot is a korean Visual-QA model based on the Google's Pix2Struct architecture. It was fine-tuned from [Deplot](https://huggingface.co/google/deplot), using korean chart image-text pairs.

ko-deplot은 Google의 Pix2Struct 구조를 기반으로 한 한국어 Visual-QA 모델입니다. [Deplot](https://huggingface.co/google/deplot) 모델을 한국어 차트 이미지-텍스트 쌍 데이터셋을 이용하여 파인튜닝하였습니다.

- **Developed by:** [NUUA](https://www.nuua.ai/en/)
- **Model type:** Visual Question Answering
- **License:** apache-2.0
- **Finetuned from model:** [google/deplot](https://huggingface.co/google/deplot)

# **Model Usage**
You can run a prediction by querying an input image together with a question as follows:

아래의 코드를 이용하여 모델 추론을 할 수 있습니다:

```python
from transformers import Pix2StructProcessor, Pix2StructForConditionalGeneration
from PIL import Image

processor = Pix2StructProcessor.from_pretrained('nuua/ko-deplot')
model = Pix2StructForConditionalGeneration.from_pretrained('nuua/ko-deplot')

IMAGE_PATH = "LOCAL_PATH_TO_IMAGE"
image = Image.open(IMAGE_PATH)

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))
```

# **Tokenizer Details**
The model's tokenizer vocab was extended from 50,344 to 65,536 tokens using the following:

- Complete Korean Jamo
- [Additional Korean Jamo](http://koreantypography.org/wp-content/uploads/2016/02/kst_12_7_2_06.pdf)
- Ko-Electra tokens

모델의 tokenizer vocab을 50344개에서 65536개로 아래를 이용하여 확장시킨 후 학습을 진행하였습니다:

- 완성형 한글 자모
- [추가 완성형 한글 자모](http://koreantypography.org/wp-content/uploads/2016/02/kst_12_7_2_06.pdf)
- Ko-Electra 한글 토큰

# **Training Details**

## Training Data

Synthetic chart data from three libraries were used:

세 개의 라이브러리에서 합성 차트 데이터를 생성하여 사용하였습니다:

- [GenPlot](https://github.com/brendanartley/genplot)
- [Chart.js](https://github.com/chartjs/Chart.js)
- [Plotly](https://github.com/plotly/plotly.py)

## Training Procedure 

The model was first exposed to a short warmup stage, following its [original paper](https://arxiv.org/pdf/2210.03347.pdf). It was then trained using the chart data for 50,000 steps.

학습을 위해 처음 짧은 "warmup" 단계를 거쳐 한글을 학습시킨 후 50,000 스텝 동안 차트 데이터를 학습시켰습니다.

# **Technical Specifications**

## Hardware

ko-deplot was trained by using A100 80G. 

A100 80G GPU를 이용하여 학습하였습니다.

# **Contact**

Any questions and suggestions, please use the discussion tab. If you want to contact us directly, email robin@nuua.ai.
{"language": ["ko", "en"], "license": "apache-2.0", "tags": ["text2text-generation"], "pipeline_tag": "visual-question-answering", "base_model": "google/deplot"}
transformers pytorch safetensors pix2struct text2text-generation visual-question-answering ko en arxiv:2210.03347 base_model:google/deplot license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us