Instructions to use isikz/esm1b_ft_phosphosite_data_combined with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use isikz/esm1b_ft_phosphosite_data_combined with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="isikz/esm1b_ft_phosphosite_data_combined")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("isikz/esm1b_ft_phosphosite_data_combined") model = AutoModelForMaskedLM.from_pretrained("isikz/esm1b_ft_phosphosite_data_combined", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
license: apache-2.0
metrics:
- perplexity
base_model:
- facebook/esm1b_t33_650M_UR50S
Finetuning on Combined Phosphosite Data
ESM-1b is finetuned by Masked Language Modeling objective. The data is combination of phosphosite data which are used to train isikz/esm1b_msa_mlm_pt_phosphosite and isikz/esm1b_mlm_pt_phosphosite. The total number of data is 1055221.