metadata
license: apache-2.0
CodeT5-small-Go_generation
This model is finetuned based on the pre-trained CodeT5-small model. This model is fine-tuned on dataset: codet5_go-generation.
5.3 upload the initial version. 5.6 upload the dataset
The model genarates the missing function body according to the input which privides the necessary class environment and an empty function.
See example below for formatting.
How to use
Here is how to use this model:
from transformers import T5ForConditionalGeneration, RobertaTokenizer
# 加载模型和tokenizer
model_path = "intm/codet5-small-go_generation"
tokenizer = RobertaTokenizer.from_pretrained('intm/codet5-small-go_generation')
model = T5ForConditionalGeneration.from_pretrained(model_path)
# 使用模型进行推理
input_text = "package names\n\nimport \"knative.dev/pkg/kmeta\"\n\n\nfunc Deployment(rev kmeta.Accessor) string {\n\treturn kmeta.ChildName(rev.GetName(), \"-deployment\")\n}\n\n\nfunc ImageCache(rev kmeta.Accessor) string {\n\treturn kmeta.ChildName(rev.GetName(), \"-cache\")\n}\n\n\n\n\nfunc PA(rev kmeta.Accessor) string"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output = model.generate(input_ids=input_ids, max_new_tokens=256) #最大长度按照数据集的max_trg_len设置
# 将生成的结果转换为字符串
output_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(output_text)
# this prints "return kmeta.ChildName(rev.GetName(), "-pa")"
Training data
YinShicheng
Training process
GuQiuhan
Advisor
Prof.WangYu
Evaluation results
TODO