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
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## **
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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**.
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The total number of data is 1055221.
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## **Finetuning on Combined Phosphosite Data**
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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**.
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The total number of data is 1055221.
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