ko-deplot

ko-deplot is a korean Visual-QA model based on the Google's Pix2Struct architecture. It was fine-tuned from Deplot, using korean chart image-text pairs.

ko-deplot은 Google의 Pix2Struct 구조를 기반으로 한 한국어 Visual-QA 모델입니다. Deplot 모델을 한국어 차트 이미지-텍스트 쌍 데이터셋을 이용하여 파인튜닝하였습니다.

• Developed by: NUUA
• Model type: Visual Question Answering
• License: apache-2.0
• Finetuned from model: 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:

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
• Ko-Electra tokens

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

• 완성형 한글 자모
• 추가 완성형 한글 자모
• 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
• Chart.js
• Plotly

Training Procedure

The model was first exposed to a short warmup stage, following its original paper. 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 [email protected].