Update README.md

README.md

@@ -77,3 +77,74 @@ pipeline = transformers.pipeline(
 text = "Insert the news article text here..."
 prediction = pipeline(text)
 print(prediction)
+```
+### **Example Prediction Output**
+
+```python
+[
+  {
+    "label": "LABEL_2",
+    "score": 0.47
+  }
+]
+
+LABEL_0: Left
+LABEL_1: Center
+LABEL_2: Right
+```
+
+### **Input / Output Details**
+
+**Input**: A single string containing the text of a news article.
+**Output**: A list of dictionaries, where each dictionary contains:
+  - "label": The predicted label (e.g., "LABEL_2")
+  - "score": The probability for that label.
+```css
+Example Output: [[{"LABEL_0": 0.23, "LABEL_1": 0.30, "LABEL_2": 0.47}]]
+Indicates 23% chance of Left, 30% chance of Center, 47% chance of Right.
+
+```
+
+## **Training & Fine-tuning**
+### **Dataset Sizes**
+
+  - **Training Set**: 17,984 examples
+  - **Evaluation Set**: 4,496 examples
+  - **Test Set**: 5,620 examples
+
+### **Hyperparameters and Important Settings**
+```python
+# Precision & Quantization
+load_in_4bit = True
+bnb_4bit_use_double_quant = True
+bnb_4bit_quant_type = "nf4"
+bnb_4bit_compute_dtype = torch.bfloat16
+
+# LoRA Configuration
+lora_r = 16
+lora_alpha = 64
+lora_dropout = 0.1
+bias = "none"
+
+# Task Type
+task_type = "SEQ_CLS"
+
+# Training Setup
+per_device_train_batch_size = 4
+gradient_accumulation_steps = 4
+learning_rate = 2e-4
+optim = "paged_adamw_32bit"
+num_train_epochs = 3
+warmup_steps = 2
+fp16 = True
+logging_steps = 1
+```
+## **Evaluation**
+### **Metrics**
+We report the F1-score on each dataset split.
+## **Results**
+
+    - F1-Score (Training): 0.96658
+    - F1-Score (Eval): 0.96664
+    - F1-Score (Test): 0.96299
+