Image-to-Text
Transformers
Safetensors
molparser_vision_encoder_decoder
image-text-to-text
chemistry
ocsr
markush
e-smiles2.0
custom_code
Instructions to use UniParser/MolParser-Mobile-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UniParser/MolParser-Mobile-V2 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="UniParser/MolParser-Mobile-V2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageTextToText model = AutoModelForImageTextToText.from_pretrained("UniParser/MolParser-Mobile-V2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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🚀 <a href="https://huggingface.co/spaces/AI4Industry/molparser-mobile">Demo</a>
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**MolParser-Mobile-V2** is a lightweight Optical Chemical Structure Recognition (OCSR) model that converts molecular structure images directly into **E-SMILES 2.0**. It upgrades MolParser-Mobile for broader recognition of structures found in chemical literature, especially complex Markush structures, while retaining a compact 10M parameter architecture.
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## 🚀 What's New
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🚀 <a href="https://huggingface.co/spaces/AI4Industry/molparser-mobile">Demo</a>
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**MolParser-Mobile-V2** is a lightweight Optical Chemical Structure Recognition (OCSR) model that converts molecular structure images directly into **E-SMILES 2.0**. It upgrades [MolParser-Mobile](https://huggingface.co/UniParser/MolParser-Mobile) for broader recognition of structures found in chemical literature, especially complex Markush structures, while retaining a compact 10M parameter architecture.
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## 🚀 What's New
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