Update README.md

README.md

@@ -36,7 +36,32 @@ Training was performed for approximately **160,000 steps**.
 
 The evaluation loss remains consistently close to the training loss throughout training (within ~0.01),  
 indicating that the model generalizes well and shows no signs of overfitting.
-
+Training arguments can be seen below:
+```python
+TRAINING_ARGS = TrainingArguments(
+    output_dir=OUTPUT_DIR,
+    overwrite_output_dir=True,
+    num_train_epochs=20,                     
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=16,
+    learning_rate=1e-4,
+    warmup_steps=500,                        
+    lr_scheduler_type="cosine",              
+    weight_decay=0.01,
+    max_grad_norm=1.0,                       
+    logging_dir=os.path.join(OUTPUT_DIR, "logs"),
+    logging_steps=100,
+    save_steps=500,
+    eval_steps=500,
+    eval_strategy="steps",
+    load_best_model_at_end=True,
+    metric_for_best_model="eval_loss",
+    greater_is_better=False,
+    save_total_limit=2,
+    fp16=True,                              
+    report_to="tensorboard",
+)
+````
 ![Training loss](./loss.png)