updated readme file

README.md

@@ -3,7 +3,7 @@ tags:
   - text-classification
   - transformers
   - biobert
-  - mRNA
+  - miRNA
   - biomedical
   - LoRA
   - fine-tuning
@@ -13,19 +13,19 @@ datasets:
 license: apache-2.0
 ---
 
-# 🧬 mRNA-BioBERT: Fine-Tuned BioBERT for mRNA Sentence Classification  
-**Fine-tuned BioBERT model for classifying mRNA-related sentences in biomedical research papers.**  
+# 🧬 miRNA-BioBERT: Fine-Tuned BioBERT for miRNA Sentence Classification  
+**Fine-tuned BioBERT model for classifying miRNA-related sentences in biomedical research papers.**  
 
-<!-- 🔗 **Hugging Face Model Link**: [debjit20504/mRNA-biobert](https://huggingface.co/debjit20504/mRNA-biobert)   -->
+<!-- 🔗 **Hugging Face Model Link**: [debjit20504/miRNA-biobert](https://huggingface.co/debjit20504/miRNA-biobert)   -->
 
 ---
 
 ## 📌 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.  
+**miRNA-BioBERT** is a fine-tuned version of [BioBERT](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1), trained specifically for **classifying sentences** as **miRNA-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**  
+✔ **Dataset**: **Curated biomedical text corpus containing labeled miRNA-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)**  
 
@@ -33,7 +33,7 @@ license: apache-2.0
 
 ## 📖 Model Applications  
 ✅ **Biomedical NLP** – Extracting meaningful information from biomedical literature.  
-✅ **mRNA Research** – Identifying sentences discussing mRNA mechanisms.  
+✅ **miRNA Research** – Identifying sentences discussing miRNA mechanisms.  
 ✅ **Automated Literature Review** – Filtering relevant studies efficiently.  
 ✅ **Genomics & Bioinformatics** – Enhancing data retrieval from scientific texts.  
 
@@ -49,7 +49,7 @@ from transformers import AutoModelForSequenceClassification, AutoTokenizer
 import torch  
 
 # Load the model and tokenizer  
-model_name = "debjit20504/mRNA-biobert"  
+model_name = "debjit20504/miRNA-biobert"  
 tokenizer = AutoTokenizer.from_pretrained(model_name)  
 model = AutoModelForSequenceClassification.from_pretrained(model_name)  
 
@@ -63,10 +63,10 @@ def classify_text(text):
     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"  
+    return "Relevant (miRNA-related)" if label == 1 else "Not Relevant"  
 
 # Example Test  
-sample_text = "mRNA translation is regulated by miRNAs."  
+sample_text = "miRNA translation is regulated by miRNAs."  
 print(f"Classification: {classify_text(sample_text)}")  
 ```