19 lines
966 B
Markdown
19 lines
966 B
Markdown
# Inst2Vec Model
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Using [HuggingFace Transformers](https://github.com/huggingface/transformers) to train a BERT with dynamic mask for Assemble Language from scratch. We name it `Inst2Vec` for it is designed to generate vectors for assemble instructions.
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It is a part of the model introduced in the ICONIP 2021 paper [A Hierarchical Graph-based Neural Network for Malware Classification](https://link.springer.com/chapter/10.1007%2F978-3-030-92273-3_51).
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The preprocessing procedure can be found in [process_data](./process_data/readme.md).
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You can simply run `python train_my_tokenizer.py` to obtain an Assemble Tokenizer.
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The script I use to train the `Inst2Vec1` model is as follows:
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```
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python my_run_mlm_no_trainer.py \
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--per_device_train_batch_size 8192 \
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--per_device_eval_batch_size 16384 \
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--num_warmup_steps 4000 --output_dir ./ \
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--seed 1234 --preprocessing_num_workers 32 \
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--max_train_steps 150000 \
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--eval_every_steps 1000
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``` |