How To Use
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}
}
- Downloads last month
- 32
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.