wei commited on
Commit
aad7b15
1 Parent(s): 1298119

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +72 -0
README.md CHANGED
@@ -6,3 +6,75 @@ widget:
6
 
7
  ---
8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6
 
7
  ---
8
 
9
+
10
+
11
+ # CodeTrans model for source code summarization sql
12
+ Pretrained model on programming language sql using the t5 large model architecture. It was first released in
13
+ [this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized sql code functions: it works best with tokenized sql functions.
14
+
15
+
16
+ ## Model description
17
+
18
+ This CodeTrans model is based on the `t5-large` 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.
19
+
20
+
21
+ ## Intended uses & limitations
22
+
23
+ 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.
24
+
25
+ ### How to use
26
+
27
+ Here is how to use this model to generate sql function documentation using Transformers SummarizationPipeline:
28
+
29
+ ```python
30
+ from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline
31
+
32
+ pipeline = SummarizationPipeline(
33
+ model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_large_source_code_summarization_sql_multitask"),
34
+ tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_large_source_code_summarization_sql_multitask", skip_special_tokens=True),
35
+ device=0
36
+ )
37
+
38
+ tokenized_code = "select time ( col0 ) from tab0"
39
+ pipeline([tokenized_code])
40
+ ```
41
+ Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/pre-training/source%20code%20summarization/sql/large_model.ipynb).
42
+ ## Training data
43
+
44
+ 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)
45
+
46
+
47
+ ## Training procedure
48
+
49
+ ### Multi-task Pretraining
50
+
51
+ The model was trained on a single TPU Pod V3-8 for 120,000 steps in total, using sequence length 512 (batch size 4096).
52
+ It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
53
+ The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.
54
+
55
+
56
+ ## Evaluation results
57
+
58
+ For the source code summarization tasks, different models achieves the following results on different programming languages (in BLEU score):
59
+
60
+ Test results :
61
+
62
+ | Language / Model | Python | SQL | C# |
63
+ | -------------------- | :------------: | :------------: | :------------: |
64
+ | CodeTrans-ST-Small | 8.45 | 17.55 | 19.74 |
65
+ | CodeTrans-ST-Base | 9.12 | 15.00 | 18.65 |
66
+ | CodeTrans-TF-Small | 10.06 | 17.71 | 20.40 |
67
+ | CodeTrans-TF-Base | 10.94 | 17.66 | 21.12 |
68
+ | CodeTrans-TF-Large | 12.41 | 18.40 | 21.43 |
69
+ | CodeTrans-MT-Small | 13.11 | 19.15 | 22.39 |
70
+ | CodeTrans-MT-Base | **13.37** | 19.24 | 23.20 |
71
+ | CodeTrans-MT-Large | 13.24 | 19.40 | **23.57** |
72
+ | CodeTrans-MT-TF-Small | 12.10 | 18.25 | 22.03 |
73
+ | CodeTrans-MT-TF-Base | 10.64 | 16.91 | 21.40 |
74
+ | CodeTrans-MT-TF-Large | 12.14 | **19.98** | 21.10 |
75
+ | CODE-NN | -- | 18.40 | 20.50 |
76
+
77
+
78
+ > 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/)
79
+
80
+