add to README model description
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README.md
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---
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tags:
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- biology
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- ibm
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- mammal
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- pytorch
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- transformers
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library_name:
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license: apache-2.0
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---
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## Model Summary
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- **Developers:** IBM Research
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- **GitHub Repository:**
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- **Paper:**
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- **Release Date**: Oct
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- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
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## Usage
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Using `
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```
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pip install
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```
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A simple example
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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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---
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tags:
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- biology
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- small-moelcule
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- single-cell-genes
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- ibm
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- mammal
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- pytorch
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- transformers
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library_name: biomed
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license: apache-2.0
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The **ibm/biomed.omics.bl.sm.ma-ted-400m** 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**), this model introduces a flexible, multi-domain architecture with an adaptable task prompt syntax.
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The syntax allows for dynamic combinations of tokens and scalars, enabling classification, regression, and generation tasks either within a single domain or with cross-domain entities.
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**TBD: add main paper figure when ready**
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## Model Summary
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- **Developers:** IBM Research
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- **GitHub Repository:** https://github.com/BiomedSciAI/biomed-multi-alignment
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- **Paper:** TBD
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- **Release Date**: Oct 28th, 2024
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- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
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## Usage
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Using `ibm/biomed.omics.bl.sm.ma-ted-400m` 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-400m`:
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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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