Instructions to use nikraf/directionality_probe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nikraf/directionality_probe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nikraf/directionality_probe", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nikraf/directionality_probe", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| all_presets_with_paths = { | |
| # ESM2 models (from esm2.py) | |
| 'ESM2-8': 'Synthyra/ESM2-8M', | |
| 'ESM2-35': 'Synthyra/ESM2-35M', | |
| 'ESM2-150': 'Synthyra/ESM2-150M', | |
| 'ESM2-650': 'Synthyra/ESM2-650M', | |
| 'ESM2-3B': 'Synthyra/ESM2-3B', | |
| # DSM models (from esm2.py) | |
| 'DSM-150': 'GleghornLab/ESM_diff_150', | |
| 'DSM-650': 'GleghornLab/ESM_diff_650', | |
| 'DSM-PPI': 'Synthyra/DSM_ppi_full', | |
| # ESMC models (from esmc.py) | |
| 'ESMC-300': 'Synthyra/ESMplusplus_small', | |
| 'ESMC-600': 'Synthyra/ESMplusplus_large', | |
| # E1 models (from e1.py) | |
| 'E1-150': 'Synthyra/Profluent-E1-150M', | |
| 'E1-300': 'Synthyra/Profluent-E1-300M', | |
| 'E1-600': 'Synthyra/Profluent-E1-600M', | |
| # ProtBert models (from protbert.py) | |
| 'ProtBert': 'Rostlab/prot_bert', | |
| 'ProtBert-BFD': 'Rostlab/prot_bert_bfd', | |
| # ProtT5 models (from prott5.py) | |
| 'ProtT5': 'Rostlab/prot_t5_xl_half_uniref50-enc', | |
| 'ProtT5-XL-UniRef50-full-prec': 'Rostlab/prot_t5_xl_uniref50', | |
| 'ProtT5-XXL-UniRef50': 'Rostlab/prot_t5_xxl_uniref50', | |
| 'ProtT5-XL-BFD': 'Rostlab/prot_t5_xl_bfd', | |
| 'ProtT5-XXL-BFD': 'Rostlab/prot_t5_xxl_bfd', | |
| # ANKH models (from ankh.py) | |
| 'ANKH-Base': 'Synthyra/ANKH_base', | |
| 'ANKH-Large': 'Synthyra/ANKH_large', | |
| 'ANKH2-Large': 'Synthyra/ANKH2_large', | |
| # GLM2 models (from glm.py) | |
| 'GLM2-150': 'tattabio/gLM2_150M', | |
| 'GLM2-650': 'tattabio/gLM2_650M', | |
| 'GLM2-GAIA': 'tattabio/gLM2_650M_embed', | |
| # DPLM models (from dplm.py) | |
| 'DPLM-150': 'airkingbd/dplm_150m', | |
| 'DPLM-650': 'airkingbd/dplm_650m', | |
| 'DPLM-3B': 'airkingbd/dplm_3b', | |
| # DPLM2 models (from dplm2.py) | |
| 'DPLM2-150': 'airkingbd/dplm2_150m', | |
| 'DPLM2-650': 'airkingbd/dplm2_650m', | |
| 'DPLM2-3B': 'airkingbd/dplm2_3b', | |
| # AMPLIFY models (from amplify.py) | |
| 'AMPLIFY-120': 'GleghornLab/AMPLIFY_120M', | |
| 'AMPLIFY-350': 'GleghornLab/AMPLIFY_350M', | |
| # Random models (from random.py) | |
| 'Random': 'random', | |
| 'Random-Transformer': 'facebook/esm2_t12_35M_UR50D', | |
| 'Random-ESM2-8': 'facebook/esm2_t6_8M_UR50D', | |
| 'Random-ESM2-35': 'facebook/esm2_t12_35M_UR50D', | |
| 'Random-ESM2-150': 'facebook/esm2_t30_150M_UR50D', | |
| 'Random-ESM2-650': 'facebook/esm2_t36_650M_UR50D', | |
| # OneHot models (from one_hot.py) - internal implementations | |
| 'OneHot-Protein': 'OneHot-Protein', | |
| 'OneHot-DNA': 'OneHot-DNA', | |
| 'OneHot-RNA': 'OneHot-RNA', | |
| 'OneHot-Codon': 'OneHot-Codon', | |
| # Vec2Vec models (from vec2vec.py) | |
| 'vec2vec-ESM2-8-ESM2-35': 'Synthyra/ESM2-8-ESM2-35-sequence-sequence', | |
| 'vec2vec-ESM2-8-ESM2-150': 'Synthyra/ESM2-8-ESM2-150-sequence-sequence', | |
| 'vec2vec-ESM2-8-ESM2-650': 'Synthyra/ESM2-8-ESM2-650-sequence-sequence', | |
| 'vec2vec-ESM2-8-ESM2-3B': 'Synthyra/ESM2-8-ESM2-3B-sequence-sequence', | |
| 'vec2vec-ESM2-35-ESM2-150': 'Synthyra/ESM2-35-ESM2-150-sequence-sequence', | |
| 'vec2vec-ESM2-35-ESM2-650': 'Synthyra/ESM2-35-ESM2-650-sequence-sequence', | |
| 'vec2vec-ESM2-35-ESM2-3B': 'Synthyra/ESM2-35-ESM2-3B-sequence-sequence', | |
| 'vec2vec-ESM2-150-ESM2-650': 'Synthyra/ESM2-150-ESM2-650-sequence-sequence', | |
| 'vec2vec-ESM2-150-ESM2-3B': 'Synthyra/ESM2-150-ESM2-3B-sequence-sequence', | |
| 'vec2vec-ESM2-650-ESM2-3B': 'Synthyra/ESM2-650-ESM2-3B-sequence-sequence', | |
| # CaLM models (from calm.py) | |
| 'CaLM': 'multimolecule/calm', | |
| } | |
| currently_supported_models = [ | |
| 'ESM2-8', | |
| 'ESM2-35', | |
| 'ESM2-150', | |
| 'ESM2-650', | |
| 'ESM2-3B', | |
| 'Random', | |
| 'Random-Transformer', | |
| 'Random-ESM2-8', | |
| 'Random-ESM2-35', # same as Random-Transformer | |
| 'Random-ESM2-150', | |
| 'Random-ESM2-650', | |
| 'ESMC-300', | |
| 'ESMC-600', | |
| 'E1-150', | |
| 'E1-300', | |
| 'E1-600', | |
| 'ProtBert', | |
| 'ProtBert-BFD', | |
| 'ProtT5', | |
| 'ProtT5-XL-UniRef50-full-prec', | |
| 'ProtT5-XXL-UniRef50', | |
| 'ProtT5-XL-BFD', | |
| 'ProtT5-XXL-BFD', | |
| 'ANKH-Base', | |
| 'ANKH-Large', | |
| 'ANKH2-Large', | |
| 'GLM2-150', | |
| 'GLM2-650', | |
| 'GLM2-GAIA', | |
| 'DPLM-150', | |
| 'DPLM-650', | |
| 'DPLM-3B', | |
| 'DPLM2-150', | |
| 'DPLM2-650', | |
| 'DPLM2-3B', | |
| 'DSM-150', | |
| 'DSM-650', | |
| 'DSM-PPI', | |
| 'OneHot-Protein', | |
| 'OneHot-DNA', | |
| 'OneHot-RNA', | |
| 'OneHot-Codon', | |
| 'AMPLIFY-120', | |
| 'AMPLIFY-350', | |
| 'CaLM', | |
| ] | |
| standard_models = [ | |
| 'AMPLIFY-120', | |
| 'AMPLIFY-350', | |
| 'GLM2-150', | |
| 'GLM2-650', | |
| 'GLM2-GAIA', | |
| 'DSM-150', | |
| 'DSM-650', | |
| 'DSM-PPI', | |
| 'E1-150', | |
| 'E1-300', | |
| 'E1-600', | |
| 'DSM-150', | |
| 'DSM-650', | |
| 'DSM-PPI', | |
| 'ESM2-8', | |
| 'ESM2-35', | |
| 'ESM2-150', | |
| 'ESM2-650', | |
| 'ESM2-3B', | |
| 'ESMC-300', | |
| 'ESMC-600', | |
| 'ProtBert-BFD', | |
| 'ProtT5', | |
| 'ANKH-Base', | |
| 'ANKH-Large', | |
| 'ANKH2-Large', | |
| 'DPLM-150', | |
| 'DPLM-650', | |
| 'DPLM-3B', | |
| 'DPLM2-150', | |
| 'DPLM2-650', | |
| 'DPLM2-3B', | |
| 'Random', | |
| 'Random-Transformer', | |
| 'OneHot-Protein', | |
| ] | |
| experimental_models = [] | |