Update README.md

README.md

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 - text: "select time ( col0 ) from tab0"
 
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+# CodeTrans model for source code summarization sql
+Pretrained model on programming language sql using the t5 small model architecture. It was first released in
+[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized sql code functions: it works best with tokenized sql functions.
+
+
+## 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 the description for the sql function or be fine-tuned on other sql code tasks. It can be used on unparsed and untokenized sql code. However, if the sql code is tokenized, the performance should be better.
+
+### How to use
+
+Here is how to use this model to generate sql function documentation using Transformers SummarizationPipeline:
+
+```python
+from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline
+
+pipeline = SummarizationPipeline(
+    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_sql_multitask"),
+    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_sql_multitask", skip_special_tokens=True),
+    device=0
+)
+
+tokenized_code = "select time ( col0 ) from tab0"
+pipeline([tokenized_code])
+```
+Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/pre-training/source%20code%20summarization/sql/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 460,000 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 source code summarization tasks, different models achieves the following results on different programming languages (in BLEU score):
+
+Test results :
+
+|   Language / Model   |     Python     |       SQL      |       C#       |
+| -------------------- | :------------: | :------------: | :------------: |
+|   CodeTrans-ST-Small    |      8.45      |     17.55      |     19.74      |
+|   CodeTrans-ST-Base     |      9.12      |     15.00      |     18.65      | 
+|   CodeTrans-TF-Small    |     10.06      |     17.71      |     20.40      |
+|   CodeTrans-TF-Base     |     10.94      |     17.66      |     21.12      |
+|   CodeTrans-TF-Large    |     12.41      |     18.40      |     21.43      |
+|   CodeTrans-MT-Small    |     13.11      |     19.15      |     22.39      |
+|   CodeTrans-MT-Base     |   **13.37**    |     19.24      |     23.20      |
+|   CodeTrans-MT-Large    |     13.24      |     19.40      |   **23.57**    |
+|   CodeTrans-MT-TF-Small |     12.10      |     18.25      |     22.03      |
+|   CodeTrans-MT-TF-Base  |     10.64      |     16.91      |     21.40      |
+|   CodeTrans-MT-TF-Large |     12.14      |   **19.98**    |     21.10      |
+|   CODE-NN   |       --       |     18.40      |     20.50      |
+
+
+> 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/)
+
+