Update README.md

README.md

@@ -1,89 +1,89 @@
----
-tags:
-  - text-classification
-  - transformers
-  - biobert
-  - miRNA
-  - biomedical
-  - LoRA
-  - fine-tuning
-library_name: transformers
-datasets:
-  - custom-biomedical-dataset
-license: apache-2.0
----
-
-# 🧬 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/miRNA-biobert](https://huggingface.co/debjit20504/miRNA-biobert)   -->
-
----
-
-## 📌 Overview  
-**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 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)**  
-
-## 🚀 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/miRNA-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 (miRNA-related)" if label == 1 else "Not Relevant"  
-
-# Example Test  
-sample_text = "miRNA 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.
-
----
-
-## 📖 Model Applications  
-✅ **Biomedical NLP** – Extracting meaningful information from biomedical literature.  
-✅ **miRNA Research** – Identifying sentences discussing miRNA mechanisms.  
-✅ **Automated Literature Review** – Filtering relevant studies efficiently.  
-✅ **Genomics & Bioinformatics** – Enhancing data retrieval from scientific texts.  
-
----
-
-## 📬 Contact
-For any questions or collaborations, reach out via:  
-
-**📧 Email**: [email protected]  
+---
+tags:
+  - text-classification
+  - transformers
+  - biobert
+  - miRNA
+  - biomedical
+  - LoRA
+  - fine-tuning
+library_name: transformers
+datasets:
+  - custom-biomedical-dataset
+license: apache-2.0
+---
+
+# 🧬 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/miRNA-biobert](https://huggingface.co/debjit20504/miRNA-biobert)   -->
+
+---
+
+## 📌 Overview  
+**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 miRNA-relevant and non-relevant sentences**  
+✔ **Task**: **Binary classification (1 = functional, 0 = non-functional)**  
+✔ **Trained on**: **RTX A6000 GPU (5 epochs, batch size 32, learning rate 2e-5)**  
+
+## 🚀 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/miRNA-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 "functional" if label == 1 else "Non-functional"  
+
+# Example Test  
+sample_text = "miRNA 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.
+
+---
+
+## 📖 Model Applications  
+✅ **Biomedical NLP** – Extracting meaningful information from biomedical literature.  
+✅ **miRNA Research** – Identifying sentences discussing miRNA mechanisms.  
+✅ **Automated Literature Review** – Filtering relevant studies efficiently.  
+✅ **Genomics & Bioinformatics** – Enhancing data retrieval from scientific texts.  
+
+---
+
+## 📬 Contact
+For any questions or collaborations, reach out via:  
+
+**📧 Email**: [email protected]  
 **🔗 LinkedIn**: https://www.linkedin.com/in/debjit-pramanik-88a837171/