Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+metrics:
+- accuracy
+tags:
+- biology
+pipeline_tag: text-classification
+---
+# Model descriptions
+PPPSL-ESM2(PPPSL, Prediction of prokaryotic protein subcellular localization) is a protein language model fine-tuned from ESM2 pretrained model [facebook/esm2_t36_3B_UR50D](https://huggingface.co/facebook/esm2_t36_3B_UR50D) on a prokaryotic protein subcellular localization dataset. It achieves the following results on the evaluation set:  
+Train Loss: 0.0015  
+Train Accuracy: 0.9893  
+Validation Loss: 0.0155  
+Validation Accuracy: 0.9702  
+Epoch: 20 
+# The dataset for training PPPSL-ESM2
+The full dataset contains 11,970 protein sequences, including Cellwall (87), Cytoplasmic (6,905), CYtoplasmic Membrane (2,567), Extracellular (1,085), Outer Membrane (758), and Periplasmic (568).
+The highly imbalanced sample sizes across the six categories in this dataset pose a significant challenge for classification.  
+# How to use
+
+### An example
+Pytorch and transformers libraries should be installed in your system.  
+### Install pytorch
+```
+pip install torch torchvision torchaudio
+
+```
+### Install transformers
+```
+pip install transformers
+
+```
+### Run the following code
+```
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load the fine-tuned model and tokenizer
+model_name = "sihuapeng/ESM2-finetuned-PPSL"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Protein sequence for prediction the subcelluar localization
+protein_sequence = "MSKKVLITGGAGYIGSVLTPILLEKGYEVCVIDNLMFDQISLLSCFHNKNFTFINGDAMDENLIRQEVAKADIIIPLAALVGAPLCKRNPKLAKMINYEAVKMISDFASPSQIFIYPNTNSGYGIGEKDAMCTEESPLRPISEYGIDKVHAEQYLLDKGNCVTFRLATVFGISPRMRLDLLVNDFTYRAYRDKFIVLFEEHFRRNYIHVRDVVKGFIHGIENYDKMKGQAYNMGLSSANLTKRQLAETIKKYIPDFYIHSANIGEDPDKRDYLVSNTKLEATGWKPDNTLEDGIKELLRAFKMMKVNRFANFN"
+
+# Encode the sequence as model input
+inputs = tokenizer(protein_sequence, return_tensors="pt")
+
+# Perform inference using the model
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# Get the prediction results
+logits = outputs.logits
+predicted_class_id = torch.argmax(logits, dim=-1).item()
+
+# Output the predicted class
+print ("===========================================================================================================================================")
+print ("ID to Label mapping: {0: 'CYtoplasmicMembrane', 1: 'Cellwall', 2: 'Cytoplasmic', 3: 'Extracellular', 4: 'OuterMembrane', 5: 'Periplasmic'}")
+print(f"Predicted class ID: {predicted_class_id}")
+print ("===========================================================================================================================================")
+```
+
+## Funding
+This project was funded by the CDC to Justin Bahl (BAA 75D301-21-R-71738).  
+### Model architecture and implementation
+[Sihua Peng](https://publichealth.uga.edu/staff/sihua-peng/)  
+## Group, Department and Institution  
+### Lab: [Justin Bahl](https://vet.uga.edu/person/justin-bahl/)  
+### Department: [College of Veterinary Medicine Department of Infectious Diseases](https://vet.uga.edu/education/academic-departments/infectious-diseases/)  
+### Institution: [The University of Georgia](https://www.uga.edu/)  
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c56e2d2d07296c7e35994f/2rlokZM1FBTxibqrM8ERs.png)