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<p align =" center " >
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<a href =" https://arxiv.org/abs/2502.11190 " >📄arXiv</a > •
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- <a href =" " >🤗HFPaper</a > •
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- <a href =" " >🎧NotebookLM Audio</a >
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+ <a href =" https://huggingface.co/papers/2502.11190 " >🤗HFPaper</a >
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</p >
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This repository provides the official PyTorch implementation of the following paper:
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+ # SemEval Unlearning
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+ This folder contains the solution developed by ZJUKLAB for the [ SemEval 2025 Task 4] ( https://llmunlearningsemeval2025.github.io/ ) competition.
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+
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+ ## Installation
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+
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+
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+ ``` bash
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+ conda create -n semeval_unlearn python=3.12
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+ conda activate semeval_unlearn
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+ pip install -r requirements.txt
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+ ```
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+
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+ ### Script Arguments
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+
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+ - ` --forget_dataset ` : Specifies the dataset to forget (must be a valid dataset path or identifier).
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+ - ` --retain_dataset ` : Specifies the dataset to retain.
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+ - ` --model_path ` : Path to the pre-trained model.
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+ - ` --output_dir ` : Directory where results and logs will be saved.
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+
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+ ### Run the Script:
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+
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+ ``` bash
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+ torchrun --nproc_per_node=1 --master_port=29500 unlearn-merging.py --forget_dataset /path/to/forget_data --retain_dataset /path/to/retain_data --model_path /path/to/model --output_dir /path/to/output
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+ ```
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+ datasets
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+ accelerate
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+ deepspeed
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+ evaluate
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+ matplotlib
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+ hydra-core
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+ omegaconf
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+ peft
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+ rouge_score
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+ tqdm
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+ matplotlib
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+ einops
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+ packaging
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+ bitsandbytes
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+ scipy
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+ ninja
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+ vllm
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+ wandb
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