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