|
--- |
|
license: bsd-3-clause |
|
--- |
|
|
|
# ProCALM |
|
[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. |
|
|
|
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 |
|
|
|
| Name | Description | |
|
|:--------|:-------:| |
|
| progen2-base | Original ProGen2 model with ~760 million parameters| |
|
| ec-onehot-uniref | Trained with onehot-encoded EC conditioning, on ~29e6 enzymes from Uniref | |
|
| ec-onehot-swissprot | Trained with onehot-encoded EC conditioning, on ~150e3 enzymes from Swissprot Train | |
|
| tax-swissprot | Trained on onehot-encoded EC taxonomy conditioning, on ~150e3 enzymes from Swissprot Train | |
|
| ec+tax-swissprot | Trained jointly on onehot-encoded EC conditioning and onehot-encoded taxonomy conditioning with parallel adapters, on ~150e3 enzymes from Swissprot Train | |
|
| ec-drfp-swissprot | Trained with DRFP-encoded EC conditioning, on ~150e3 enzymes from Swissprot Train | |
|
| ec-creep-swissprot | Trained with CREEP-encoded EC conditioning, on ~150e3 enzymes from Swissprot Train | |
|
|
|
More usage details can be found in [github](https://github.com/jsunn-y/ProCALM/tree/main) and in our paper. |
