Update README.md

README.md

@@ -22,6 +22,51 @@ Can do abstractive summarization of legal/contractual documents. Fine tuned on B
 
 - Abstractive summarization for legal docs (Banking, Legal, Contractual, etc.)
 
+## Sample Usage
+
+Load model config and safetensors:
+```python
+from transformers import BartForConditionalGeneration, BartTokenizer
+import torch
+
+
+model_name = "siddheshtv/bart-multi-lexsum"
+
+model = BartForConditionalGeneration.from_pretrained(model_name)
+tokenizer = BartTokenizer.from_pretrained(model_name)
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = model.to(device)
+```
+
+Generate Summary Function
+```python
+def generate_summary(model, tokenizer, text, max_length=512):
+    device = next(model.parameters()).device
+    inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=1024, truncation=True)
+    inputs = inputs.to(device)
+    summary_ids = model.generate(
+        inputs,
+        max_length=max_length,
+        min_length=40,
+        length_penalty=2.0,
+        num_beams=4,
+        early_stopping=True,
+        no_repeat_ngram_size=3,
+        forced_bos_token_id=0,
+        forced_eos_token_id=2
+    )
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+```
+
+Generate summary
+```python
+generated_summary = generate_summary(model, tokenizer, example_text)
+print("Generated Summary:")
+print(generated_summary)
+```
+
 ## Training Data
 
 - **Dataset URL:** [Multi-Lexsum](https://multilexsum.github.io/)