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README.md

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 ---
-tags:
-- summarization
-widget:
-- text: "def e ( message , exit_code = None ) : print_log ( message , YELLOW , BOLD ) if exit_code is not None : sys . exit ( exit_code )"
-
+language: code
 ---
-
-
-# CodeTrans model for code documentation generation python
-Pretrained model on programming language python using the t5 small model architecture. It was first released in
-[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized python code functions: it works best with tokenized python functions.
-
-
-## Model description
-
-This CodeTrans model is based on the `t5-small` model. It has its own SentencePiece vocabulary model. It used transfer-learning pre-training on 7 unsupervised datasets in the software development domain. It is then fine-tuned on the code documentation generation task for the python function/method.
-
-## Intended uses & limitations
-
-The model could be used to generate the description for the python function or be fine-tuned on other python code tasks. It can be used on unparsed and untokenized python code. However, if the python code is tokenized, the performance should be better.
-
-### How to use
-
-Here is how to use this model to generate python function documentation using Transformers SummarizationPipeline:
-
-```python
-from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline
-
-pipeline = SummarizationPipeline(
-    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_code_documentation_generation_python_transfer_learning_finetune"),
-    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_code_documentation_generation_python_transfer_learning_finetune", skip_special_tokens=True),
-    device=0
-)
-
-tokenized_code = "def e ( message , exit_code = None ) : print_log ( message , YELLOW , BOLD ) if exit_code is not None : sys . exit ( exit_code )"
-pipeline([tokenized_code])
-```
-Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/transfer%20learning%20fine-tuning/function%20documentation%20generation/python/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
-
-### Transfer-learning Pretraining
-
-The model was trained on a single TPU Pod V3-8 for half million 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.
-
-### Fine-tuning
-
-This model was then fine-tuned on a single TPU Pod V2-8 for 2000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing python code.
-
-
-## Evaluation results
-
-For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):
-
-Test results :
-
-|   Language / Model   |     Python     |      Java      |       Go       |      Php       |      Ruby      |   JavaScript   |
-| -------------------- | :------------: | :------------: | :------------: | :------------: | :------------: | :------------: |
-|   CodeTrans-ST-Small    |      17.31     |     16.65      |     16.89      |     23.05      |      9.19      |      13.7      |
-|   CodeTrans-ST-Base     |      16.86     |     17.17      |     17.16      |     22.98      |      8.23      |      13.17     |   
-|   CodeTrans-TF-Small    |      19.93     |     19.48      |     18.88      |     25.35      |     13.15      |      17.23     |
-|   CodeTrans-TF-Base     |      20.26     |     20.19      |     19.50      |     25.84      |     14.07      |      18.25     |
-|   CodeTrans-TF-Large    |      20.35     |     20.06      |   **19.54**    |     26.18      |     14.94      |    **18.98**   |
-|   CodeTrans-MT-Small    |      19.64     |     19.00      |     19.15      |     24.68      |     14.91      |      15.26     |
-|   CodeTrans-MT-Base     |    **20.39**   |     21.22      |     19.43      |   **26.23**    |   **15.26**    |      16.11     |
-|   CodeTrans-MT-Large    |      20.18     |   **21.87**    |     19.38      |     26.08      |     15.00      |      16.23     |
-|   CodeTrans-MT-TF-Small |      19.77     |     20.04      |     19.36      |     25.55      |     13.70      |      17.24     |
-|   CodeTrans-MT-TF-Base  |      19.77     |     21.12      |     18.86      |     25.79      |     14.24      |      18.62     |
-|   CodeTrans-MT-TF-Large |      18.94     |     21.42      |     18.77      |     26.20      |     14.19      |      18.83     |
-|   State of the art   |      19.06     |     17.65      |     18.07      |     25.16      |     12.16      |      14.90     |
-
-
-> 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/)
-