Transformers
PyTorch
t5
text2text-generation
protein-generation
sequence-generation
conditional-generation
text-generation-inference
Instructions to use MoMA-LAAS/prop2seq-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MoMA-LAAS/prop2seq-model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("MoMA-LAAS/prop2seq-model") model = AutoModelForSeq2SeqLM.from_pretrained("MoMA-LAAS/prop2seq-model") - Notebooks
- Google Colab
- Kaggle
File size: 1,085 Bytes
c2adc59 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | {
"mean": {
"Rg": 14.92135140993968,
"ete": 36.51412726240827,
"scaling_exponent": 0.5339325049079684,
"asphericity": 0.4559565227880063,
"prefactor": 6.1388136438092085,
"SHD": 2.5293296323763963,
"FCR": 0.24488553975833108,
"KL_hydropathy": -0.48035638511997825,
"kappa*": -0.4252169368049604,
"omega*": 0.0754381675942535,
"SCD": 0.21557541074810355,
"Length": 33.53519297326951,
"Fract_pos": 0.12313853392284548,
"Fract_neg": 0.12174700583548557,
"NCPR": 0.001391528087359907
},
"std": {
"Rg": 6.614851573439867,
"ete": 15.422660980400911,
"scaling_exponent": 0.0535883014001273,
"asphericity": 0.03324832067125699,
"prefactor": 0.6254380024980856,
"SHD": 0.7088175174516826,
"FCR": 0.10873439661879275,
"KL_hydropathy": 0.810189166439004,
"kappa*": 1.157971124069066,
"omega*": 0.6659491393910585,
"SCD": 1.5063322615485712,
"Length": 26.25489301310707,
"Fract_pos": 0.08248835096056287,
"Fract_neg": 0.0898743042163313,
"NCPR": 0.13394128637978803
}
} |