Ezi commited on
Commit
9b54e42
1 Parent(s): ff8a875

Model Card

Browse files

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

)

Files changed (1) hide show
  1. README.md +76 -16
README.md CHANGED
@@ -8,19 +8,29 @@ widget:
8
 
9
 
10
  # CodeTrans model for program synthesis
11
- Pretrained model on programming language lisp inspired DSL using the t5 small model architecture. It was first released in
12
- [this repository](https://github.com/agemagician/CodeTrans).
13
 
14
-
15
- ## Model description
16
-
17
- 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.
18
-
19
- ## Intended uses & limitations
20
-
21
- The model could be used to generate lisp inspired DSL code given the human language description tasks.
22
-
23
- ### How to use
 
 
 
 
 
 
 
 
 
 
 
 
24
 
25
  Here is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:
26
 
@@ -42,20 +52,44 @@ Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/bl
42
  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)
43
 
44
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
45
  ## Training procedure
46
 
47
- ### Transfer-learning Pretraining
 
 
48
 
49
  The model was trained on a single TPU Pod V3-8 for 500,000 steps in total, using sequence length 512 (batch size 4096).
50
  It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
51
  The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.
52
 
53
- ### Fine-tuning
54
 
55
  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.
56
 
57
 
58
- ## Evaluation results
 
 
59
 
60
  For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):
61
 
@@ -77,6 +111,32 @@ Test results :
77
  | State of the art | 85.80 |
78
 
79
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
80
 
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/)
82
 
 
8
 
9
 
10
  # CodeTrans model for program synthesis
 
 
11
 
12
+ ## Table of Contents
13
+ - [Model Details](#model-details)
14
+ - [How to Get Started With the Model](#how-to-get-started-with-the-model)
15
+ - [Uses](#uses)
16
+ - [Risks, Limitations and Biases](#risks-limitations-and-biases)
17
+ - [Training](#training)
18
+ - [Evaluation](#evaluation)
19
+ - [Environmental Impact](#environmental-impact)
20
+ - [Citation Information](#citation-information)
21
+
22
+ ## Model Details
23
+ - **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.
24
+ - **Developed by:** [Ahmed Elnaggar](https://www.linkedin.com/in/prof-ahmed-elnaggar/),[Wei Ding](https://www.linkedin.com/in/wei-ding-92561270/)
25
+ - **Model Type:** Summarization
26
+ - **Language(s):** English
27
+ - **License:** Unknown
28
+ - **Resources for more information:**
29
+ - [Research Paper](https://arxiv.org/pdf/2104.02443.pdf)
30
+ - [GitHub Repo](https://github.com/agemagician/CodeTrans)
31
+
32
+
33
+ ## How to Get Started With the Model
34
 
35
  Here is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:
36
 
 
52
  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)
53
 
54
 
55
+
56
+
57
+ ## Uses
58
+
59
+ #### Direct Use
60
+
61
+ The model could be used to generate lisp inspired DSL code given the human language description tasks.
62
+
63
+
64
+ ## Training
65
+
66
+ #### Training Data
67
+
68
+ 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)
69
+
70
+ The authors provide additionally notes about the vocabulary used, in the [associated paper](https://arxiv.org/pdf/2104.02443.pdf):
71
+
72
+ > We used the SentencePiece model (Kudo, 2018) to construct the vocabulary for this research, as well as to decode and encode the input/output.
73
+
74
+
75
  ## Training procedure
76
 
77
+ #### Preprocessing
78
+
79
+ ##### Transfer-learning Pretraining
80
 
81
  The model was trained on a single TPU Pod V3-8 for 500,000 steps in total, using sequence length 512 (batch size 4096).
82
  It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
83
  The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.
84
 
85
+ ###### Fine-tuning
86
 
87
  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.
88
 
89
 
90
+ ## Evaluation
91
+
92
+ #### Results
93
 
94
  For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):
95
 
 
111
  | State of the art | 85.80 |
112
 
113
 
114
+ ## Environmental Impact
115
+
116
+ 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).
117
+
118
+
119
+ - **Hardware Type:** Nvidia RTX 8000 GPUs
120
+
121
+ - **Hours used:** Unknown
122
+
123
+ - **Cloud Provider:** GCC TPU v2-8 and v3-8.
124
+
125
+ - **Compute Region:** Unknown
126
+
127
+ - **Carbon Emitted:** Unknown
128
+
129
+ ## Citation Information
130
+
131
+ ```bibtex
132
+ @misc{elnaggar2021codetrans,
133
+ title={CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing},
134
+ 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},
135
+ year={2021},
136
+ eprint={2104.02443},
137
+ archivePrefix={arXiv},
138
+ primaryClass={cs.SE}
139
+ }
140
+ ```
141
 
 
142