Instructions to use brainventures/deplot_kr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brainventures/deplot_kr with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="brainventures/deplot_kr")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("brainventures/deplot_kr") model = AutoModelForMultimodalLM.from_pretrained("brainventures/deplot_kr") - Notebooks
- Google Colab
- Kaggle
dltjwl commited on
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Parent(s): 1f5289b
ADD : Preprocessing
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README.md
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@@ -14,8 +14,10 @@ deplot_kr은 google의 pix2struct 구조를 기반으로 한 한국어 image-to-
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## How to use
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You can run a prediction by input an image.
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Model predict the data table of text form in the image.
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```python
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from transformers import Pix2StructForConditionalGeneration, Pix2StructImageProcessor, AutoTokenizer, Pix2StructProcessor
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### Preprocessing
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### Train
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The model was trained in a TPU environment.
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## How to use
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You can run a prediction by input an image.
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Model predict the data table of text form in the image.
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이미지를 모델에 입력하면 모델은 이미지로부터 표 형태의 데이터 테이블을 예측합니다.
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```python
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from transformers import Pix2StructForConditionalGeneration, Pix2StructImageProcessor, AutoTokenizer, Pix2StructProcessor
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### Preprocessing
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[Liu et al.(2023)](https://arxiv.org/pdf/2212.10505.pdf) 에 따라,
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According to [Liu et al.(2023)](https://arxiv.org/pdf/2212.10505.pdf)...
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- markdown format
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- | : seperating cells (열 구분)
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- \n : seperating rows (행 구분)
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### Train
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The model was trained in a TPU environment.
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