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README.md

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 ---
-library_name: transformers
-tags: []
+license: cc-by-nc-nd-4.0
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
+# PepDoRA: A Unified Peptide-Specific Language Model via Weight-Decomposed Low-Rank Adaptation
 
-[More Information Needed]
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64cd5b3f0494187a9e8b7c69/fzsxEjCdBJfKa6T44Tjc8.png)
 
-**APA:**
+In this work, we introduce **PepDoRA**, a SMILES transformer that fine-tunes the state-of-the-art [ChemBERTa-77M-MLM](https://huggingface.co/DeepChem/ChemBERTa-77M-MLM) transformer on modified peptide SMILES via [DoRA](https://nbasyl.github.io/DoRA-project-page/), a novel PEFT method that incorporates weight decomposition. These representations can be leveraged for numerous downstream tasks, including membrane permeability prediction and target binding assessment, for both unmodified and modified peptide sequences.
 
-[More Information Needed]
+Here's how to extract PepDoRA embeddings for your input peptide:
 
-## Glossary [optional]
+```
+import torch
+from transformers import AutoTokenizer, AutoModel
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+# Load the model and tokenizer
+model_name = "ChatterjeeLab/PepDoRA"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name, output_hidden_states=True)
+   
 
-[More Information Needed]
+# Input peptide sequence
+peptide = "CC(C)C[C@H]1NC(=O)[C@@H](C)NCCCCCCNC(=O)[C@H](CO)NC1=O"
 
-## More Information [optional]
+# Tokenize the peptide
+inputs = tokenizer(peptide, return_tensors="pt")
 
-[More Information Needed]
+# Get the hidden states (embeddings) from the model
+with torch.no_grad():
+    outputs = model(**inputs,output_hidden_states=True)
+   
+# Extract the embeddings from the last hidden layer
+embeddng=outputs.last_hidden_state
 
-## Model Card Authors [optional]
+# Print the embedding shape (or the embedding itself)
+print(outputs.last_hidden_state.shape)
+print(embeddng)
+```
 
-[More Information Needed]
+## Repository Authors
 
-## Model Card Contact
+[Leyao Wang](mailto:leyao.wang@vanderbilt.edu), Undergraduate Intern in the Chatterjee Lab <br>
+[Pranam Chatterjee](mailto:pranam.chatterjee@duke.edu), Assistant Professor at Duke University 
 
-[More Information Needed]
+Reach out to us with any questions!