| --- |
| tags: |
| - summarization |
| widget: |
| - text: "you are given an array of numbers a and a number b , compute the difference of elements in a and b" |
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|
| --- |
| |
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| # CodeTrans model for program synthesis |
| Pretrained model on programming language lisp inspired DSL using the t5 small model architecture. It was first released in |
| [this repository](https://github.com/agemagician/CodeTrans). |
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| ## Model description |
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| This CodeTrans model is based on the `t5-small` model. It has its own SentencePiece vocabulary model. It used multi-task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets. |
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| ## Intended uses & limitations |
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| The model could be used to generate lisp inspired DSL code given the human language description tasks. |
|
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| ### How to use |
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| Here is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline: |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline |
| |
| pipeline = SummarizationPipeline( |
| model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_program_synthese_multitask"), |
| tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_program_synthese_multitask", 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](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/pre-training/program%20synthesis/small_model.ipynb). |
| ## Training data |
|
|
| The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1) |
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|
| ## Training procedure |
|
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| ### Multi-task Pretraining |
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| The model was trained on a single TPU Pod V3-8 for 440,000 steps in total, using sequence length 512 (batch size 4096). |
| It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. |
| The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training. |
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| ## Evaluation results |
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| For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score): |
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| Test results : |
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| | Language / Model | LISP | |
| | -------------------- | :------------: | |
| | CodeTrans-ST-Small | 89.43 | |
| | CodeTrans-ST-Base | 89.65 | |
| | CodeTrans-TF-Small | 90.30 | |
| | CodeTrans-TF-Base | 90.24 | |
| | CodeTrans-TF-Large | 90.21 | |
| | CodeTrans-MT-Small | 82.88 | |
| | CodeTrans-MT-Base | 86.99 | |
| | CodeTrans-MT-Large | 90.27 | |
| | CodeTrans-MT-TF-Small | **90.31** | |
| | CodeTrans-MT-TF-Base | 90.30 | |
| | CodeTrans-MT-TF-Large | 90.17 | |
| | State of the art | 85.80 | |
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| > Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/) |
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