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  # ProCALM
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  [ProCALM](https://github.com/jsunn-y/ProCALM/tree/main) (Protein Conditionally Adapted Language Model) is a suite of models where [ProGen2-base](https://github.com/enijkamp/progen2) is finetuned with conditional adapters for conditional generation of functional enzymes, based on EC number, taxonomy, or both.
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- ProCALM models share `tokenizer.json` and individual models are organized into subfolders. We have uploaded the most relevant models here, but please reach out if you would like to use other models from our paper. `1.5B` and `9B` refer to checkpoints trained to 1.5 and 9 billion tokens, respectively
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  | Name | Description |
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  |:--------|:-------:|
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  | ec-drfp-swissprot | Trained with DRFP-encoded EC conditioning, on ~150e3 enzymes from Swissprot Train |
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  | ec-creep-swissprot | Trained with CREEP-encoded EC conditioning, on ~150e3 enzymes from Swissprot Train |
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- More usage details can be found in [github](https://github.com/jsunn-y/ProCALM/tree/main) and in our paper.
 
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  # ProCALM
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  [ProCALM](https://github.com/jsunn-y/ProCALM/tree/main) (Protein Conditionally Adapted Language Model) is a suite of models where [ProGen2-base](https://github.com/enijkamp/progen2) is finetuned with conditional adapters for conditional generation of functional enzymes, based on EC number, taxonomy, or both.
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+ ProCALM models share `tokenizer.json` and individual models are organized into subfolders. We have uploaded the most relevant models here, but please reach out if you would like to use other models from our paper. `1.5B` and `9B` refer to checkpoints trained to 1.5 and 9 billion tokens, respectively. More usage details can be found in [github](https://github.com/jsunn-y/ProCALM/tree/main) and in our paper.
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  | Name | Description |
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  |:--------|:-------:|
 
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  | ec-drfp-swissprot | Trained with DRFP-encoded EC conditioning, on ~150e3 enzymes from Swissprot Train |
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  | ec-creep-swissprot | Trained with CREEP-encoded EC conditioning, on ~150e3 enzymes from Swissprot Train |
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