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README.md
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license: apache-2.0
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---
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The **ibm/biomed.omics.bl.sm.ma-ted-
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Designed for robust performance, it achieves state-of-the-art results over a variety of tasks across the entire drug discovery pipeline and the diverse biomedical domains.
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Based on the **M**olecular **A**ligned **M**ulti-**M**odal **A**rchitecture and **L**anguage (**MAMMAL**), a flexible, multi-domain architecture with an adaptable task prompt syntax.
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## Usage
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Using `ibm/biomed.omics.bl.sm.ma-ted-
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```
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pip install git+https://github.com/BiomedSciAI/biomed-multi-alignment.git
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```
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A simple example for a task already supported by `ibm/biomed.omics.bl.sm.ma-ted-
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```python
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import torch
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from fuse.data.tokenizers.modular_tokenizer.op import ModularTokenizerOp
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from mammal.keys import *
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# Load Model
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model = Mammal.from_pretrained("ibm/biomed.omics.bl.sm.ma-ted-
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# Load Tokenizer
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tokenizer_op = ModularTokenizerOp.from_pretrained("ibm/biomed.omics.bl.sm.ma-ted-
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# Prepare Input Prompt
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protein_calmodulin = "MADQLTEEQIAEFKEAFSLFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMISELDQDGFIDKEDLHDGDGKISFEEFLNLVNKEMTADVDGDGQVNYEEFVTMMTSK"
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license: apache-2.0
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---
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The **ibm/biomed.omics.bl.sm.ma-ted-458m** model is a biomedical foundation model trained on over 2 billion biological samples across multiple modalities, including proteins, small molecules, and single-cell gene data.
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Designed for robust performance, it achieves state-of-the-art results over a variety of tasks across the entire drug discovery pipeline and the diverse biomedical domains.
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Based on the **M**olecular **A**ligned **M**ulti-**M**odal **A**rchitecture and **L**anguage (**MAMMAL**), a flexible, multi-domain architecture with an adaptable task prompt syntax.
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## Usage
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Using `ibm/biomed.omics.bl.sm.ma-ted-458m` requires installing [https://github.com/BiomedSciAI/biomed-multi-alignment](https://github.com/TBD)
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```
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pip install git+https://github.com/BiomedSciAI/biomed-multi-alignment.git
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```
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A simple example for a task already supported by `ibm/biomed.omics.bl.sm.ma-ted-458m`:
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```python
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import torch
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from fuse.data.tokenizers.modular_tokenizer.op import ModularTokenizerOp
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from mammal.keys import *
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# Load Model
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model = Mammal.from_pretrained("ibm/biomed.omics.bl.sm.ma-ted-458m")
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# Load Tokenizer
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tokenizer_op = ModularTokenizerOp.from_pretrained("ibm/biomed.omics.bl.sm.ma-ted-458m")
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# Prepare Input Prompt
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protein_calmodulin = "MADQLTEEQIAEFKEAFSLFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMISELDQDGFIDKEDLHDGDGKISFEEFLNLVNKEMTADVDGDGQVNYEEFVTMMTSK"
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