Update README.md

README.md

@@ -5,3 +5,74 @@ widget:
 - text: "you are given an array of numbers a and a number b , compute the difference of elements in a and b"
 
 ---
+
+
+# CodeTrans model for program synthesis
+Pretrained model on programming language lisp inspired DSL 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 lisp inspired DSL code given the human language description tasks.
+
+### How to use
+
+Here is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:
+
+```python
+from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline
+
+pipeline = SummarizationPipeline(
+    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_program_synthese_multitask"),
+    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_program_synthese_multitask", skip_special_tokens=True),
+    device=0
+)
+
+tokenized_code = "you are given an array of numbers a and a number b , compute the difference of elements in a and b"
+pipeline([tokenized_code])
+```
+Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/pre-training/program%20synthesis/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 440,000 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   |      LISP      |
+| -------------------- | :------------: |
+|   CodeTrans-ST-Small    |     89.43      |
+|   CodeTrans-ST-Base     |     89.65      |
+|   CodeTrans-TF-Small    |     90.30      |
+|   CodeTrans-TF-Base     |     90.24      |
+|   CodeTrans-TF-Large    |     90.21      |
+|   CodeTrans-MT-Small    |     82.88      |
+|   CodeTrans-MT-Base     |     86.99      |
+|   CodeTrans-MT-Large    |     90.27      |
+|   CodeTrans-MT-TF-Small |   **90.31**    |
+|   CodeTrans-MT-TF-Base  |     90.30      |
+|   CodeTrans-MT-TF-Large |     90.17      |
+|   State of the art   |     85.80      |
+
+
+
+> 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/)
+