SEBIS
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code_trans_t5_small_api_generation_multitask

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-tags:
-- summarization
-widget:
-- text: "parse the uses licence node of this package , if any , and returns the license definition if theres"
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-# 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](https://github.com/agemagician/CodeTrans).
-## Model description
-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.
-## 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:
-```python
-from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline
-pipeline = SummarizationPipeline(
-    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_api_generation_multitask"),
-    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_api_generation_multitask", 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](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/pre-training/api%20generation/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)
-## Training procedure
-### Multi-task Pretraining
-The model was trained on a single TPU Pod V3-8 for 500,000 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.
-## 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](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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+language: code
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