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README.md

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+# 🧬 mRNA-BioBERT: Fine-Tuned BioBERT for mRNA Sentence Classification  
+**Fine-tuned BioBERT model for classifying mRNA-related sentences in biomedical research papers.**  
+
+🔗 **Hugging Face Model Link**: [debjit20504/mRNA-biobert](https://huggingface.co/debjit20504/mRNA-biobert)  
+
 ---
-license: mit
+
+## 📌 Overview  
+**mRNA-BioBERT** is a fine-tuned version of [BioBERT](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1), trained specifically for **classifying sentences** as **mRNA-related (relevant) or not (irrelevant)**. The model is useful for **automating literature reviews**, **extracting relevant sentences**, and **identifying key insights** in genomic research.  
+
+✔ **Base Model**: `dmis-lab/biobert-base-cased-v1.1`  
+✔ **Fine-tuning Method**: **LoRA (Low-Rank Adaptation)**  
+✔ **Dataset**: **Curated biomedical text corpus containing labeled mRNA-relevant and non-relevant sentences**  
+✔ **Task**: **Binary classification (1 = relevant, 0 = not relevant)**  
+✔ **Trained on**: **RTX A6000 GPU (5 epochs, batch size 32, learning rate 2e-5)**  
+
 ---
+
+## 📖 Model Applications  
+✅ **Biomedical NLP** – Extracting meaningful information from biomedical literature.  
+✅ **mRNA Research** – Identifying sentences discussing mRNA mechanisms.  
+✅ **Automated Literature Review** – Filtering relevant studies efficiently.  
+✅ **Genomics & Bioinformatics** – Enhancing data retrieval from scientific texts.  
+
+---
+
+## 🚀 How to Use the Model  
+### 1️⃣ Install Dependencies  
+```bash  
+pip install transformers torch
+```
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer  
+import torch  
+
+# Load the model and tokenizer  
+model_name = "debjit20504/mRNA-biobert"  
+tokenizer = AutoTokenizer.from_pretrained(model_name)  
+model = AutoModelForSequenceClassification.from_pretrained(model_name)  
+
+# Move model to GPU or MPS (for Mac)  
+device = torch.device("mps") if torch.backends.mps.is_available() else torch.device("cuda" if torch.cuda.is_available() else "cpu")  
+model.to(device)  
+model.eval()
+
+def classify_text(text):  
+    inputs = tokenizer(text, return_tensors="pt").to(device)  
+    with torch.no_grad():  
+        output = model(**inputs)  
+        label = torch.argmax(output.logits, dim=1).item()  
+    return "Relevant (mRNA-related)" if label == 1 else "Not Relevant"  
+
+# Example Test  
+sample_text = "mRNA translation is regulated by miRNAs."  
+print(f"Classification: {classify_text(sample_text)}")  
+```
+
+## 📊 Training Details
+- Dataset: Biomedical text dataset with 429,785 relevant sentences and 87,966 irrelevant sentences.
+- Fine-Tuning Method: LoRA (Low-Rank Adaptation) for efficient training.
+- Training Hardware: NVIDIA RTX A6000 GPU.
+- Training Settings:
+    - Batch size: 32
+    - Learning rate: 2e-5
+    - Optimizer: AdamW
+    - Warmup steps: 1000
+    - Epochs: 5
+    - Mixed precision (fp16): ✅ Enabled for efficiency.
+
+## 📬 Contact
+For any questions or collaborations, reach out via:  
+
+**📧 Email**: debjit.pramanik@postgrad.manchester.ac.uk  
+**🔗 LinkedIn**: https://www.linkedin.com/in/debjit-pramanik-88a837171/