Update README.md

README.md

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 - text: "protected String renderUri ( URI uri ) { return uri . toASCIIString ( ) ; }"
 
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+
+
+# CodeTrans model for code documentation generation java
+Pretrained model on programming language java using the t5 base model architecture. It was first released in
+[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized java code functions: it works best with tokenized java functions.
+
+
+## Model description
+
+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 code comment generation task for the java function/method.
+
+## Intended uses & limitations
+
+The model could be used to generate the description for the java function or be fine-tuned on other java code tasks. It can be used on unparsed and untokenized java code. However, if the java code is tokenized, the performance should be better.
+
+### 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_base_code_comment_generation_java_multitask_finetune"),
+    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_code_comment_generation_java_multitask_finetune", skip_special_tokens=True),
+    device=0
+)
+
+tokenized_code = "protected String renderUri ( URI uri ) { return uri . toASCIIString ( ) ; }"
+pipeline([tokenized_code])
+```
+Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/fine-tuning/code%20comment%20generation/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
+
+### Multi-task Pretraining
+
+The model was trained on a single TPU Pod V3-8 for 260,000 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.
+
+### Fine-tuning
+
+This model was then fine-tuned on a single TPU Pod V2-8 for 60,000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing java 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   |      Java      |
+| -------------------- | :------------: |
+|   CodeTrans-ST-Small    |     37.98      |
+|   CodeTrans-ST-Base     |     38.07      |
+|   CodeTrans-TF-Small    |     38.56      |
+|   CodeTrans-TF-Base     |     39.06      |
+|   CodeTrans-TF-Large    |   **39.50**    |
+|   CodeTrans-MT-Small    |     20.15      |
+|   CodeTrans-MT-Base     |     27.44      |
+|   CodeTrans-MT-Large    |     34.69      |
+|   CodeTrans-MT-TF-Small |     38.37      |
+|   CodeTrans-MT-TF-Base  |     38.90      |
+|   CodeTrans-MT-TF-Large |     39.25      |
+|   State of the art   |     38.17      |
+
+
+
+> 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/)
+
+