Update README.md

README.md

@@ -1,199 +1,62 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+metrics:
+- perplexity
+base_model:
+- facebook/esm1b_t33_650M_UR50S
 ---
 
-# Model Card for Model ID
+## **Fine-Tuning ESM-1b with Multiple Sequence Alignment (MSA) for Phosphosites**
 
-<!-- Provide a quick summary of what the model is/does. -->
+This repository provides a fine-tuned version of ESM-1b, incorporating genomic information by leveraging long phosphosite sequences from [DARKIN dataset](https://openreview.net/pdf?id=a4x5tbYRYV) and Multiple Sequence Alignment (MSA) of those phosphosites. The goal is to enhance the model's understanding of phosphorylation by integrating sequence conservation patterns.
 
+### Developed by:
 
+Zeynep Işık (MSc, Sabanci University)
 
-## Model Details
 
-### Model Description
+### **Dataset & Preprocessing**
+To construct a robust dataset, we extracted 256 MSA sequences per phosphosite from publicly available sequence databases. This resulted in a dataset of approximately 2 million sequences. Due to the large data size, the following preprocessing steps were applied:
 
-<!-- Provide a longer summary of what this model is. -->
+1. Selection of MSA Sequences for Labeled Data
+  - Up to 10 MSA sequences were selected per human phosphosite.
+  - This resulted in a final dataset of 98,000 samples.
+2. Dataset Splitting
+  - 10% of the data was reserved for validation.
+  - The remaining 90% was used for fine-tuning with the Masked Language Modeling (MLM) objective.
+3. Data Processing & Preprocessing
+  - Special attention was given to retaining phosphorylation residues within sequences.
+  - To optimize memory efficiency, sequence lengths were truncated to 128 amino acids.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+### Evaluation
 
-- **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]
+Perplexity: 2.69 (decreased from 7.05)
 
-### Model Sources [optional]
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+import torch
 
-<!-- Provide the basic links for the model. -->
+### Usage
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+```
+# Load the model and tokenizer
+model_name = "isikz/phosphosite_msa_finetuned_esm1b"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForMaskedLM.from_pretrained(model_name)
 
-## Uses
+# Example sequence with a masked residue
+sequence = "MKTLLLTLVVV[MASK]VCLDLGYTGV"
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+# Tokenize input
+inputs = tokenizer(sequence, return_tensors="pt")
 
-### Direct Use
+# Get prediction
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_token_id = torch.argmax(logits[0, 10]).item()  # Assuming MASK is at position 10
+    predicted_token = tokenizer.decode([predicted_token_id])
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+print(f"Predicted Residue: {predicted_token}")
+```
 
-[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]
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-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
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-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
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-### 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]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## 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:**
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-[More Information Needed]
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-**APA:**
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-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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