language: code
tags:
- summarization
widget:
- text: >-
parse the uses licence node of this package , if any , and returns the
license definition if theres
CodeTrans model for api recommendation generation
Pretrained model for api recommendation generation using the t5 base model architecture. It was first released in this repository.
Model description
This CodeTrans model is based on the t5-base model. It has its own SentencePiece vocabulary model. It used single-task training on Api Recommendation Generation dataset.
Intended uses & limitations
The model could be used to generate api usage for the java programming tasks.
How to use
Here is how to use this model to generate java function documentation using Transformers SummarizationPipeline:
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline
pipeline = SummarizationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_api_generation"),
tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_api_generation", skip_special_tokens=True),
device=0
)
tokenized_code = "parse the uses licence node of this package , if any , and returns the license definition if theres"
pipeline([tokenized_code])
Run this example in colab notebook.
Training data
The supervised training tasks datasets can be downloaded on Link
Evaluation results
For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):
Test results :
| Language / Model | Java |
|---|---|
| CodeTrans-ST-Small | 68.71 |
| CodeTrans-ST-Base | 70.45 |
| CodeTrans-TF-Small | 68.90 |
| CodeTrans-TF-Base | 72.11 |
| CodeTrans-TF-Large | 73.26 |
| CodeTrans-MT-Small | 58.43 |
| CodeTrans-MT-Base | 67.97 |
| CodeTrans-MT-Large | 72.29 |
| CodeTrans-MT-TF-Small | 69.29 |
| CodeTrans-MT-TF-Base | 72.89 |
| CodeTrans-MT-TF-Large | 73.39 |
| State of the art | 54.42 |
Created by Ahmed Elnaggar | LinkedIn and Wei Ding | LinkedIn