Pretrained model on programming language lisp inspired DSL using the t5 base model architecture. It was first released in this repository.
This CodeTrans model is based on the
t5-base model. It has its own SentencePiece vocabulary model. It used single-task training on Program Synthesis dataset.
The model could be used to generate lisp inspired DSL code based on the human language description tasks.
Here is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline pipeline = SummarizationPipeline( model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_program_synthese"), tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_program_synthese", skip_special_tokens=True), device=0 ) tokenized_code = "you are given an array of numbers a and a number b , compute the difference of elements in a and b" pipeline([tokenized_code])
Run this example in colab notebook.
The supervised training tasks datasets can be downloaded on Link
For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):
Test results :
|Language / Model||LISP|
|State of the art||85.80|
Select AutoNLP in the “Train” menu to fine-tune this model automatically.
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