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--- |
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tags: |
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- summarization |
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widget: |
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- text: "parse the uses licence node of this package , if any , and returns the license definition if theres" |
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--- |
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# CodeTrans model for api recommendation generation |
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Pretrained model for api recommendation generation using the t5 base model architecture. It was first released in |
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[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-base` 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. It is then fine-tuned on the api recommendation generation task for the java apis. |
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## Intended uses & limitations |
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The model could be used to generate api usage for the java programming tasks. |
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### How to use |
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Here is how to use this model to generate java function documentation using Transformers SummarizationPipeline: |
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```python |
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from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline |
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pipeline = SummarizationPipeline( |
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model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_api_generation_multitask_finetune"), |
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tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_api_generation_multitask_finetune", skip_special_tokens=True), |
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device=0 |
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) |
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tokenized_code = "parse the uses licence node of this package , if any , and returns the license definition if theres" |
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pipeline([tokenized_code]) |
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``` |
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Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/fine-tuning/api%20generation/base_model.ipynb). |
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## Training data |
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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 500,000 steps in total, using sequence length 512 (batch size 4096). |
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It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. |
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The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training. |
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### Fine-tuning |
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This model was then fine-tuned on a single TPU Pod V2-8 for 320,000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing api recommendation generation data. |
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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 | Java | |
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| -------------------- | :------------: | |
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| CodeTrans-ST-Small | 68.71 | |
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| CodeTrans-ST-Base | 70.45 | |
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| CodeTrans-TF-Small | 68.90 | |
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| CodeTrans-TF-Base | 72.11 | |
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| CodeTrans-TF-Large | 73.26 | |
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| CodeTrans-MT-Small | 58.43 | |
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| CodeTrans-MT-Base | 67.97 | |
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| CodeTrans-MT-Large | 72.29 | |
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| CodeTrans-MT-TF-Small | 69.29 | |
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| CodeTrans-MT-TF-Base | 72.89 | |
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| CodeTrans-MT-TF-Large | **73.39** | |
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| State of the art | 54.42 | |
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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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