Upload README.md

README.md

@@ -1,3 +1,63 @@
----
-license: apache-2.0
----
+# How To Use
+```PYTHON
+from transformers import BartForConditionalGeneration, BartTokenizer
+model = BartForConditionalGeneration.from_pretrained("NTUYG/ComFormer")
+tokenizer = BartTokenizer.from_pretrained("NTUYG/ComFormer")
+code = '''    
+public static void copyFile( File in, File out )  
+            throws IOException  
+    {  
+        FileChannel inChannel = new FileInputStream( in ).getChannel();  
+        FileChannel outChannel = new FileOutputStream( out ).getChannel();  
+        try
+        {  
+//          inChannel.transferTo(0, inChannel.size(), outChannel);      // original -- apparently has trouble copying large files on Windows  
+ 
+            // magic number for Windows, 64Mb - 32Kb)  
+            int maxCount = (64 * 1024 * 1024) - (32 * 1024);  
+            long size = inChannel.size();  
+            long position = 0;  
+            while ( position < size )  
+            {  
+               position += inChannel.transferTo( position, maxCount, outChannel );  
+            }  
+        }  
+        finally
+        {  
+            if ( inChannel != null )  
+            {  
+               inChannel.close();  
+            }  
+            if ( outChannel != null )  
+            {  
+                outChannel.close();  
+            }  
+        }  
+    }
+    '''
+code_seq, sbt = utils.transformer(code) #can find in https://github.com/NTDXYG/ComFormer
+input_text = code_seq + sbt
+input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=256, 		truncation=True)
+summary_text_ids = model.generate(
+    input_ids=input_ids,
+    bos_token_id=model.config.bos_token_id,
+    eos_token_id=model.config.eos_token_id,
+    length_penalty=2.0,
+    max_length=30,
+    min_length=2,
+    num_beams=5,
+)
+comment = tokenizer.decode(summary_text_ids[0], skip_special_tokens=True)
+print(comment)
+```
+# BibTeX entry and citation info
+```
+@misc{yang2021comformer,
+      title={ComFormer: Code Comment Generation via Transformer and Fusion Method-based Hybrid Code Representation}, 
+      author={Guang Yang and Xiang Chen and Jinxin Cao and Shuyuan Xu and Zhanqi Cui and Chi Yu and Ke Liu},
+      year={2021},
+      eprint={2107.03644},
+      archivePrefix={arXiv},
+      primaryClass={cs.SE}
+}
+```