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  <!-- Provide a quick summary of what the model is/does. -->
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- CLASPP is a ESM2-150m protein lanuguage model that can pred PTM envents occuring on the substrate based
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  off primary protein sequence. This is done on multiple differnt PTM types (12) as a form of multi-label
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  classifcation. The encoder is training on a supervised Contrastive learing task then the classifcation
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  head is finetunted on the multi-label classifcation.
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  | Repo | Link | Discription|
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  | ------------- | ------------- |------------------------------------------|
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- | GitHub | [github version Data_cur](https://github.com/gravelCompBio/Claspp_data_cur/tree/main) | This verstion contains code but needs the to run the code to generate all the helper-files (will take some time run this code)|
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  | Zenodo | [zenodo version Data_cur](https://github.com/gravelCompBio/Claspp_data_cur/tree/main) | This version contains code and helper files already genrated. mostly for proof of concept and seeing the all the data intermeidate states |
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- | GitHub | [github version Forward](https://github.com/gravelCompBio/Claspp_data_cur/tree/main) | This verstion contains code but NOT any weights (file to big)|
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  | Huggingface | [huggingface version Forward](https://huggingface.co/gravelcompbio/Claspp) | This verstion contains code and training weights |
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  | Zenodo | [zenodo version training_data](https://github.com/gravelCompBio/Claspp_data_cur/tree/main) | zenodo version of training/testing/validation data|
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- | Hugingface | [huggingface version training_data](https://github.com/gravelCompBio/Claspp_data_cur/tree/main) | huggingface version of training/testing/validation data|
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- | Hugingface | [gradio lab webtool](https://github.com/gravelCompBio/Claspp_data_cur/tree/main) | webtool hosted on gradio lab (huggingface)|
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- | webtool | [website version of webtool](https://github.com/gravelCompBio/Claspp_data_cur/tree/main) | wettool hosted on a server|
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  - **Repository:** [More Information Needed]
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  - **Paper [optional]:** [More Information Needed]
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  ### Creating this conda environment
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- (yml file is include but torch sometimes makes it not useable depending on your nvidia driver)
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  Just type these lines of code into the terminal after you download this repository (this assumes you have [anaconda](https://www.anaconda.com/) already installed)
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  <!-- Provide a quick summary of what the model is/does. -->
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+ CLASPP is a ESM2-150m protein lanuguage model that can predict PTM envents occuring on the substrate based
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  off primary protein sequence. This is done on multiple differnt PTM types (12) as a form of multi-label
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  classifcation. The encoder is training on a supervised Contrastive learing task then the classifcation
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  head is finetunted on the multi-label classifcation.
 
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  | Repo | Link | Discription|
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  | ------------- | ------------- |------------------------------------------|
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+ | GitHub | [github version Data_cur](https://github.com/gravelCompBio/Claspp_data_cur/tree/main) | This verstion contains code but but no data. It needs you to run the code to generate all the helper-files (will take some time run this code)|
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  | Zenodo | [zenodo version Data_cur](https://github.com/gravelCompBio/Claspp_data_cur/tree/main) | This version contains code and helper files already genrated. mostly for proof of concept and seeing the all the data intermeidate states |
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+ | GitHub | [github version Forward](https://github.com/gravelCompBio/Claspp_data_cur/tree/main) | This verstion contains code but NOT any weights (file to big for github)|
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  | Huggingface | [huggingface version Forward](https://huggingface.co/gravelcompbio/Claspp) | This verstion contains code and training weights |
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  | Zenodo | [zenodo version training_data](https://github.com/gravelCompBio/Claspp_data_cur/tree/main) | zenodo version of training/testing/validation data|
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+ | webtool | [website version of webtool](https://github.com/gravelCompBio/Claspp_data_cur/tree/main) | webtool hosted on a server|
 
 
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  - **Repository:** [More Information Needed]
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  - **Paper [optional]:** [More Information Needed]
 
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  ### Creating this conda environment
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+ our nvidia driver)
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  Just type these lines of code into the terminal after you download this repository (this assumes you have [anaconda](https://www.anaconda.com/) already installed)
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