CodeTrans model for api recommendation generation

Pretrained model for api recommendation generation using the t5 small model architecture. It was first released in this repository.

Model description

This CodeTrans model is based on the t5-small 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_small_api_generation"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_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

Downloads last month
3
Hosted inference API
Summarization