Update README.md

README.md

@@ -5,3 +5,77 @@ widget:
 - text: "public static function update ( $ table ) { if ( ! is_array ( $ table ) ) { $ table = json_decode ( $ table , true ) ; } if ( ! SchemaManager :: tableExists ( $ table [ 'oldName' ] ) ) { throw SchemaException :: tableDoesNotExist ( $ table [ 'oldName' ] ) ; } $ updater = new self ( $ table ) ; $ updater -> updateTable ( ) ; }"
 
 ---
+
+# CodeTrans model for code documentation generation php
+Pretrained model on programming language php using the t5 base model architecture. It was first released in
+[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized php code functions: it works best with tokenized php functions.
+
+
+## Model description
+
+This CodeTrans model is based on the `t5-base` 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 php function/method.
+
+## Intended uses & limitations
+
+The model could be used to generate the description for the php function or be fine-tuned on other php code tasks. It can be used on unparsed and untokenized php code. However, if the php code is tokenized, the performance should be better.
+
+### How to use
+
+Here is how to use this model to generate php function documentation using Transformers SummarizationPipeline:
+
+```python
+from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline
+
+pipeline = SummarizationPipeline(
+    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_code_documentation_generation_php_transfer_learning_finetune"),
+    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_code_documentation_generation_php_transfer_learning_finetune", skip_special_tokens=True),
+    device=0
+)
+
+tokenized_code = "public static function update ( $ table ) { if ( ! is_array ( $ table ) ) { $ table = json_decode ( $ table , true ) ; } if ( ! SchemaManager :: tableExists ( $ table [ 'oldName' ] ) ) { throw SchemaException :: tableDoesNotExist ( $ table [ 'oldName' ] ) ; } $ updater = new self ( $ table ) ; $ updater -> updateTable ( ) ; }"
+
+pipeline([tokenized_code])
+```
+Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/transfer%20learning%20fine-tuning/function%20documentation%20generation/php/base_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 65,000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing php 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/)
+
+