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  This repository provides PyTorch source code associated with our publication, "A Mamba-Based Foundation Model for Chemistry".
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- **Paper:** [Arxiv Link](https://openreview.net/pdf?id=HTgCs0KSTl)
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- **HuggingFace:** [HuggingFace Link](https://huggingface.co/ibm/materials.smi_ssed)
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  For more information contact: [email protected] or [email protected].
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  ### Pretrained Models and Training Logs
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- We provide checkpoints of the SMI-SSED model pre-trained on a dataset of ~91M molecules curated from PubChem. The pre-trained model shows competitive performance on classification and regression benchmarks from MoleculeNet. For model weights: [HuggingFace Link](https://huggingface.co/ibm/materials.smi-ted)
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  Add the SMI-SSED `pre-trained weights.pt` to the `inference/` or `finetune/` directory according to your needs. The directory structure should look like the following:
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  This repository provides PyTorch source code associated with our publication, "A Mamba-Based Foundation Model for Chemistry".
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+ **Paper NeurIPS AI4Mat 2024:** [Arxiv Link](https://openreview.net/pdf?id=HTgCs0KSTl)
 
 
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  For more information contact: [email protected] or [email protected].
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  ### Pretrained Models and Training Logs
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+ We provide checkpoints of the SMI-SSED model pre-trained on a dataset of ~91M molecules curated from PubChem. The pre-trained model shows competitive performance on classification and regression benchmarks from MoleculeNet.
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  Add the SMI-SSED `pre-trained weights.pt` to the `inference/` or `finetune/` directory according to your needs. The directory structure should look like the following:
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