mirror of
https://github.com/syssec-utd/pylingual.git
synced 2026-05-10 18:39:03 -07:00
Joel-Flores123
49 lines
1.5 KiB
Markdown
49 lines
1.5 KiB
Markdown
# Model Training
|
||
|
||
PyLingual's accuracy is dependent on having accurate segmentation and statement models [^1]. The segmentation model divides a list of bytecode instructions into groups for each source instruction. The statement model transforms each group of instructions into source code. The instructions for training these models is as follows:
|
||
|
||
## Dataset generation
|
||
|
||
Create a dataset JSON file based off the sample (`sample_jsons/py36-sample-data.json`).
|
||
|
||
The dataset directory should be structured like so, with only one `.py` file per directory:
|
||
|
||
```
|
||
dataset
|
||
├── 0
|
||
│ └── file.py
|
||
├── 1
|
||
│ └── file.py
|
||
...
|
||
├── 999
|
||
│ └── file.py
|
||
└── 1000
|
||
└── file.py
|
||
```
|
||
|
||
The names of the inner directories and files do not matter. Then create the dataset:
|
||
|
||
```
|
||
uv run prepare_dataset.py <path to JSON>
|
||
```
|
||
|
||
## Segmentation model
|
||
|
||
Create a segmentation model JSON file based off the sample (`sample_jsons/py36-sample-segmentation.json`). Then train the model:
|
||
|
||
```
|
||
uv run train_models.py --segmentation <path to JSON>
|
||
```
|
||
|
||
## Statement model
|
||
|
||
Create a statement model JSON file based off the sample (`sample_jsons/py36-sample-statement.json`). Then train the model:
|
||
|
||
```
|
||
uv run train_models.py --statement <path to JSON>
|
||
```
|
||
|
||
Once models are trained, update `../pylingual/decompiler_config.yaml` or create a separate config file by replacing the old models with the newly trained ones.
|
||
|
||
[^1]: [pylingual models](https://huggingface.co/syssec-utd).
|