Model Card

Hi!👋 This PR has a some additional information for the model card, based on the format we are using as part of our effort to standardise model cards at Hugging Face. Feel free to merge if you are ok with the changes! (cc

@Marissa



@Meg



@Nazneen

)

README.md

@@ -8,19 +8,29 @@ widget:
 
 
 # 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 transfer-learning pre-training on 7 unsupervised datasets in the software development domain. It is then fine-tuned on the program synthesis task for the lisp inspired DSL code.
-
-## Intended uses & limitations
-
-The model could be used to generate lisp inspired DSL code given the human language description tasks.
-
-### How to use
+## Table of Contents
+- [Model Details](#model-details)
+- [How to Get Started With the Model](#how-to-get-started-with-the-model)
+- [Uses](#uses)
+- [Risks, Limitations and Biases](#risks-limitations-and-biases)
+- [Training](#training)
+- [Evaluation](#evaluation)
+- [Environmental Impact](#environmental-impact)
+- [Citation Information](#citation-information)
+
+## Model Details
+- **Model Description:** This CodeTrans model is based on the `t5-small` 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 program synthesis task for the lisp inspired DSL code.
+- **Developed by:** [Ahmed Elnaggar](https://www.linkedin.com/in/prof-ahmed-elnaggar/),[Wei Ding](https://www.linkedin.com/in/wei-ding-92561270/)
+- **Model Type:** Summarization
+- **Language(s):** English
+- **License:** Unknown
+- **Resources for more information:**
+	- [Research Paper](https://arxiv.org/pdf/2104.02443.pdf)
+    - [GitHub Repo](https://github.com/agemagician/CodeTrans)
+
+
+## How to Get Started With the Model
 
 Here is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:
 
@@ -42,20 +52,44 @@ Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/bl
 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)
 
 
+
+
+## Uses
+
+#### Direct Use
+
+The model could be used to generate lisp inspired DSL code given the human language description tasks.
+
+
+## Training
+
+#### 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)
+
+The authors provide additionally notes about the vocabulary used, in the [associated paper](https://arxiv.org/pdf/2104.02443.pdf): 
+
+> We used the SentencePiece model (Kudo, 2018) to construct the vocabulary for this research, as well as to decode and encode the input/output.
+
+
 ## Training procedure
 
-### Transfer-learning Pretraining
+#### Preprocessing
+
+##### Transfer-learning 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
+###### Fine-tuning
 
 This model was then fine-tuned on a single TPU Pod V2-8 for 5,000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing lisp inspired DSL data.
 
 
-## Evaluation results
+## Evaluation
+
+#### Results
 
 For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):
 
@@ -77,6 +111,32 @@ Test results :
 |   State of the art   |     85.80      |
 
 
+## Environmental Impact
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). We present the hardware type based on the [associated paper](https://arxiv.org/pdf/2105.09680.pdf).
+
+
+- **Hardware Type:** Nvidia RTX 8000 GPUs
+
+- **Hours used:** Unknown
+
+- **Cloud Provider:** GCC TPU v2-8 and v3-8. 
+
+- **Compute Region:** Unknown
+
+- **Carbon Emitted:** Unknown 
+
+## Citation Information
+
+```bibtex
+@misc{elnaggar2021codetrans,
+      title={CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing}, 
+      author={Ahmed Elnaggar and Wei Ding and Llion Jones and Tom Gibbs and Tamas Feher and Christoph Angerer and Silvia Severini and Florian Matthes and Burkhard Rost},
+      year={2021},
+      eprint={2104.02443},
+      archivePrefix={arXiv},
+      primaryClass={cs.SE}
+}
+```
 
-> 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/)