Added use example

README.md

@@ -45,4 +45,25 @@ Comparison with `distilbert-base-uncased-finetuned-sst-2-english`:
 |Mann Whithney U-test|0.00108|0.00108|
 |Student t-test | 1.33e-12 	 | 3.03e-12|
 
-Comparison with `xlm-roberta-large` yielded inconclusive results; whereas accuracy was outperformed by this model, the macro F1 score was not. Neither outperformance was statistically significant.
+Comparison with `xlm-roberta-large` yielded inconclusive results; whereas accuracy was outperformed by this model, the macro F1 score was not. Neither outperformance was statistically significant.
+
+## Use examples
+
+```python
+from simpletransformers.classification import ClassificationModel
+model_args = {
+        "num_train_epochs": 6,
+        "learning_rate": 3e-6,
+        "train_batch_size": 69}
+
+model = ClassificationModel(
+    "roberta", "5roop/roberta-base-frenk-hate", use_cuda=True,
+    args=model_args
+    
+)
+
+predictions, logit_output = model.predict(["Build the wall", "Build the wall of trust"])
+predictions
+### Output:
+### array([1, 0])
+```