Update README.md

README.md

@@ -26,7 +26,33 @@ Conclusion: Personalized impressions generated by PEGASUS were clinically useful
 ## 🚀 Usage
 
 ```bash
-python generate 
+finetuned_model = "xtie/BART-PET-impression"
+tokenizer = AutoTokenizer.from_pretrained(finetuned_model) 
+model = AutoModelForSeq2SeqLM.from_pretrained(finetuned_model, ignore_mismatched_sizes=True).eval()
+
+findings_info =
+"""
+Description: PET CT WHOLE BODY
+Radiologist: James
+Findings: Head/Neck: xxx Chest: xxx Abdomen/Pelvis: xxx Extremities/Musculoskeletal: xxx
+Indication: The patient is a [AGE]-year old [SEX] with a history of xxx
+"""
+
+inputs = tokenizer(findings_info, padding="max_length", truncation=True, max_length=1024, return_tensors="pt")
+input_ids = inputs.input_ids.to("cuda")
+attention_mask = inputs.attention_mask.to("cuda")
+outputs = model.generate(input_ids, attention_mask=attention_mask, max_new_tokens=512, 
+                                                                   num_beam_groups=1,
+                                                                   num_beams=4, 
+                                                                   do_sample=False,
+                                                                   diversity_penalty=0.0,
+                                                                   num_return_sequences=1, 
+                                                                   length_penalty=2.0,
+                                                                   no_repeat_ngram_size=3,
+                                                                   early_stopping=True)
+
+# all special tokens including will be removed
+output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True) # get the generated impressions
 ```