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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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--- |
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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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from mammal.model import Mammal |
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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-400m") |
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# Load Tokenizer |
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tokenizer_op = ModularTokenizerOp.from_pretrained("ibm/biomed.omics.bl.sm.ma-ted-400m") |
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# Prepare Input Prompt |
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protein_calmodulin = "MADQLTEEQIAEFKEAFSLFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMISELDQDGFIDKEDLHDGDGKISFEEFLNLVNKEMTADVDGDGQVNYEEFVTMMTSK" |
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protein_calcineurin = "MSSKLLLAGLDIERVLAEKNFYKEWDTWIIEAMNVGDEEVDRIKEFKEDEIFEEAKTLGTAEMQEYKKQKLEEAIEGAFDIFDKDGNGYISAAELRHVMTNLGEKLTDEEVDEMIRQMWDQNGDWDRIKELKFGEIKKLSAKDTRGTIFIKVFENLGTGVDSEYEDVSKYMLKHQ" |
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# Create and load sample |
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sample_dict = dict() |
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# Formatting prompt to match pre-training syntax |
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sample_dict[ENCODER_INPUTS_STR] = f"<@TOKENIZER-TYPE=AA><BINDING_AFFINITY_CLASS><SENTINEL_ID_0><MOLECULAR_ENTITY><MOLECULAR_ENTITY_GENERAL_PROTEIN><SEQUENCE_NATURAL_START>{protein_calmodulin}<SEQUENCE_NATURAL_END><MOLECULAR_ENTITY><MOLECULAR_ENTITY_GENERAL_PROTEIN><SEQUENCE_NATURAL_START>{protein_calcineurin}<SEQUENCE_NATURAL_END><EOS>" |
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# Tokenize |
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tokenizer_op( |
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sample_dict=sample_dict, |
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key_in=ENCODER_INPUTS_STR, |
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key_out_tokens_ids=ENCODER_INPUTS_TOKENS, |
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key_out_attention_mask=ENCODER_INPUTS_ATTENTION_MASK, |
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) |
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sample_dict[ENCODER_INPUTS_TOKENS] = torch.tensor(sample_dict[ENCODER_INPUTS_TOKENS]) |
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sample_dict[ENCODER_INPUTS_ATTENTION_MASK] = torch.tensor(sample_dict[ENCODER_INPUTS_ATTENTION_MASK]) |
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# Generate Prediction |
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batch_dict = model.generate( |
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[sample_dict], |
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output_scores=True, |
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return_dict_in_generate=True, |
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max_new_tokens=5, |
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) |
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# Get output |
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generated_output = tokenizer_op._tokenizer.decode(batch_dict[CLS_PRED][0]) |
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print(f"{generated_output=}") |
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``` |
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For more advanced usage, see our detailed example at: <LINK> |
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## Citation |
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If you found our work useful, please consider to give a star to the repo and cite our paper: |
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``` |
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@article{TBD, |
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title={TBD}, |
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author={IBM Research Team}, |
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jounal={arXiv preprint arXiv:TBD}, |
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year={2024} |
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} |
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``` |
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