Image-to-Text
Transformers
Safetensors
English
idefics3
image-text-to-text
chemistry
ocr
chemical-structure
document-understanding
vision-language-model
patent-analysis
smoldocling
Instructions to use docling-project/ChemicalOCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use docling-project/ChemicalOCR 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/ChemicalOCR")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("docling-project/ChemicalOCR") model = AutoModelForImageTextToText.from_pretrained("docling-project/ChemicalOCR") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4001675f50f0dfe6357e246b0203dd8d501c80e7a7c6f06a40c9a225aba0f373
- Size of remote file:
- 5.5 kB
- SHA256:
- 7badb25ddb926a41445cac07985916781f101a258ceb71a5482f1d45945be3ec
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.