Image-to-Text
Transformers
PyTorch
English
udop
text-generation
chemistry
markush
cxsmiles
molecular-structure
ocr
document-understanding
vision-language-model
patent-analysis
Instructions to use docling-project/MarkushGrapher-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use docling-project/MarkushGrapher-2 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="docling-project/MarkushGrapher-2")# Load model directly from transformers import AutoProcessor, AutoModelForSeq2SeqLM processor = AutoProcessor.from_pretrained("docling-project/MarkushGrapher-2") model = AutoModelForSeq2SeqLM.from_pretrained("docling-project/MarkushGrapher-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e7463c1c6b7c46ae0de79b004f3d7318bf4307ef0bef4de3be2e38ae80fab160
- Size of remote file:
- 792 kB
- SHA256:
- d60acb128cf7b7f2536e8f38a5b18a05535c9e14c7a355904270e15b0945ea86
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