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Joel-Flores123
49 lines
1.5 KiB
Markdown
49 lines
1.5 KiB
Markdown
# Model Training
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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:
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## Dataset generation
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Create a dataset JSON file based off the sample (`sample_jsons/py36-sample-data.json`).
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The dataset directory should be structured like so, with only one `.py` file per directory:
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```
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dataset
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├── 0
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│ └── file.py
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├── 1
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│ └── file.py
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...
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├── 999
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│ └── file.py
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└── 1000
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└── file.py
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```
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The names of the inner directories and files do not matter. Then create the dataset:
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```
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uv run prepare_dataset.py <path to JSON>
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```
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## Segmentation model
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Create a segmentation model JSON file based off the sample (`sample_jsons/py36-sample-segmentation.json`). Then train the model:
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```
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uv run train_models.py --segmentation <path to JSON>
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```
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## Statement model
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Create a statement model JSON file based off the sample (`sample_jsons/py36-sample-statement.json`). Then train the model:
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```
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uv run train_models.py --statement <path to JSON>
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```
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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.
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[^1]: [pylingual models](https://huggingface.co/syssec-utd).
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