model=T5ForConditionalGeneration.from_pretrained(Salesforce/codet5-base) #定义输入代码和目标风格 input_code=deffunction_name(x):\n#Docstring\n\\\Thisisafunctiondocstring.\\\ nreturnx*2 target_style=google #将输入代码转换为模型可以理解的格式 input...
CodeT5、INCODER、CodeX 方法 生成整个修复函数 生成整个修复函数就是将有bug的函数直接输入给模型,然后模型输出修复后的数据,但是由于预训练模型的预训练数据里没有APR数据,所有直接给喂数据,效果可能不好,所以作者又构建了前缀模板来做in-context learning,这里作者用的是one-shot 修复代码填充 作者参考掩码语言模型...
transformer t5模型指导文件 备注:t5forconditionalgeneration与t5model有差异,两者不大一样 t5forconditionalgeneration中间有一个将encoder部分降维的过程 hidden_states = encoder_outputs[0] if self.model_parallel: torch.cuda.set_device(self.decoder.first_device) if labels is not None and decoder_input_ids...
最近,像CodeBERT和CodeT5这样的预训练语言模型在代码摘要生成方面表现最好。 然而,LLM现在在许多问题中通常优于预训练的较小的模型。 Ahmed和Devanbu[3]报告说,LLM可以用一个简单的提示,只包含同一个项目中的几个样本,就胜过预训练的语言模型。 这项工作说明了,谨慎建造提示词结构(即“提示词工程”)的前景。
Learn the basics of Lambda with an AWS SDK PDF Focus mode There are more AWS SDK examples available in the AWS Doc SDK Examples GitHub repo. The following code examples show how to: Create an IAM role and Lambda function, then upload handler code. Invoke the function with...
You can simply load the model (CodeT5-small and CodeT5-base) and do the inference: from transformers import RobertaTokenizer, T5ForConditionalGeneration tokenizer = RobertaTokenizer.from_pretrained('Salesforce/codet5-base') model = T5ForConditionalGeneration.from_pretrained('Salesforce/codet5-base')...
在长文本实验中,将Z-Code++与PEGASUS和LongT5进行比较后可以看到,Z-coder++依然是SOTA,并将平均最高分提升了0.35,而参数量还不到LongT5-3B的三分之一。 在XSum排行榜进行人工评价后,Z-coder++仍然从整体上来看是最高分,达到0.51 在多语言文摘评价上,研究人员在GEM基准上进行测试后,可以发现Z-coder++用了更少...
model_name: the name of the model, currently supportcodet5andcausal-lm. model_type: type of model for each model name, e.g.base,codegen-350M-mono,j-6B, etc. load_in_8bitandload_in_4bit: inherit the dynamic quantization feature fromHuggingface Quantization. ...
However, the performance improvement over CodeT5 features is negligible if we consider the advantages of automatically inferring features. Finally, our ML model surpassed less experienced annotators and nearly matched the most experienced annotator, suggesting it can assist less experienced developers under...
CodeT5 EMNLP 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation 文中对CodeT5的描述是:a unified pre-trained encoder-decoder Transformer model that better leverages the code semantics conveyed from the developer-assigned identifiers,即一个能更好地...