Update README.md

README.md

@@ -54,17 +54,40 @@ Precision Mode: FP16 for efficient training
 
 ### Test Set Performance:
 
-SARI: 89.76
+| Metric              | Score |
+|---------------------|-------|
+| SARI                | 89.76 |
+| SARI Penalized      | 88.32 |
+| ROUGE-1             | 93.14 |
+| ROUGE-2             | 88.65 |
+| ROUGE-L             | 93.07 |
+
+---
+### Training Loss Curve
 
-SARI Penalized: 88.32
+The loss curves during training are visualized in bart-large-sentence-compression_loss.eps, showing both training and evaluation loss over steps.
 
-ROUGE-1: 93.14
+---
+## **Usage**
 
-ROUGE-2: 88.65
+### Load the Model
 
-ROUGE-L: 93.07
+```python
+from transformers import BartForConditionalGeneration, BartTokenizer
 
----
-### Training Loss Curve
+model_name = "shahin-as/bart-large-sentence-compression"
+
+model = BartForConditionalGeneration.from_pretrained(model_name)
+tokenizer = BartTokenizer.from_pretrained(model_name)
+
+def compress_sentence(sentence):
+    inputs = tokenizer(sentence, return_tensors="pt", max_length=1024, truncation=True)
+    summary_ids = model.generate(**inputs, max_length=50, num_beams=5, length_penalty=2.0, early_stopping=True)
+    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
 
-The loss curves during training are visualized in bart-large-sentence-compression_loss.eps, showing both training and evaluation loss over steps.
+# Example usage
+sentence = "Insert the sentence to be compressed here."
+compressed_sentence = compress_sentence(sentence)
+print("Original:", sentence)
+print("Compressed:", compressed_sentence)
+```