Update README.md

README.md

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+
+
+# CodeTrans model for git commit message generation
+Pretrained model on git commit using the t5 small model architecture. It was first released in
+[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized git commit: it works best with tokenized git commit.
+
+
+## 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. It is then fine-tuned on the git commit message generation task for the java commit changes.
+
+## Intended uses & limitations
+
+The model could be used to generate the git commit message for the git commit changes or be fine-tuned on other relevant tasks. It can be used on unparsed and untokenized commit changes. However, if the change is tokenized, the performance should be better.
+
+### How to use
+
+Here is how to use this model to generate git commit message using Transformers SummarizationPipeline:
+
+```python
+from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline
+
+pipeline = SummarizationPipeline(
+    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_commit_generation_multitask_finetune"),
+    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_commit_generation_multitask_finetune", skip_special_tokens=True),
+    device=0
+)
+
+tokenized_code = "new file mode 100644 index 000000000 . . 892fda21b Binary files / dev / null and b / src / plugins / gateway / lib / joscar . jar differ"
+pipeline([tokenized_code])
+```
+Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/fine-tuning/commit%20generation/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 500,000 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 8,000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing commit changes.
+
+
+## Evaluation results
+
+For the git commit message generation task, different models achieves the following results on different programming languages (in BLEU score):
+
+Test results :
+
+|   Language / Model   |      Java      |
+| -------------------- | :------------: |
+|   CodeTrans-ST-Small    |     39.61      |
+|   CodeTrans-ST-Base     |     38.67      |
+|   CodeTrans-TF-Small    |     44.22      |
+|   CodeTrans-TF-Base     |     44.17      |
+|   CodeTrans-TF-Large    |   **44.41**    |
+|   CodeTrans-MT-Small    |     36.17      |
+|   CodeTrans-MT-Base     |     39.25      |
+|   CodeTrans-MT-Large    |     41.18      |
+|   CodeTrans-MT-TF-Small |     43.96      |
+|   CodeTrans-MT-TF-Base  |     44.19      |
+|   CodeTrans-MT-TF-Large |     44.34      |
+|   State of the art   |     32.81      |
+
+
+
+> 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/)
+