from datasets import ( GeneratorBasedBuilder, SplitGenerator, DownloadManager, BuilderConfig, ) import json import os import datasets from typing import List _HOMEPAGE = "http://github.com/iamgroot42/mimir" _DESCRIPTION = """\ Member and non-member splits for our MI experiments using MIMIR. Data is available for each source. """ _CITATION = """\ @article{duan2024membership, title={Do Membership Inference Attacks Work on Large Language Models?}, author={Michael Duan and Anshuman Suri and Niloofar Mireshghallah and Sewon Min and Weijia Shi and Luke Zettlemoyer and Yulia Tsvetkov and Yejin Choi and David Evans and Hannaneh Hajishirzi}, year={2024}, journal={arXiv:2402.07841}, } """ _DOWNLOAD_URL = "https://huggingface.co/datasets/iamgroot42/mimir/resolve/main/" class MimirConfig(BuilderConfig): """BuilderConfig for Mimir dataset.""" def __init__(self, *args, subsets: List[str] = [], **kwargs): """Constructs a MimirConfig. Args: **kwargs: keyword arguments forwarded to super. """ super(MimirConfig, self).__init__(**kwargs) self.subsets = subsets class MimirDataset(GeneratorBasedBuilder): VERSION = datasets.Version("1.3.0") BUILDER_CONFIG_CLASS = MimirConfig BUILDER_CONFIGS = [ MimirConfig( name="arxiv", subsets=["ngram_7_0.2", "ngram_13_0.2", "ngram_13_0.8"], description="This split contains data from the Pile's Arxiv subset at various n-gram overlap thresholds" ), MimirConfig( name="dm_mathematics", subsets=["ngram_7_0.2", "ngram_13_0.2", "ngram_13_0.8"], description="This split contains data from the Pile's DM Mathematics subset at various n-gram overlap thresholds" ), MimirConfig( name="github", subsets=["ngram_7_0.2", "ngram_13_0.2", "ngram_13_0.8"], description="This split contains data from the Pile's GitHub subset at various n-gram overlap thresholds" ), MimirConfig( name="hackernews", subsets=["ngram_7_0.2", "ngram_13_0.2", "ngram_13_0.8"], description="This split contains data from the Pile's HackerNews subset at various n-gram overlap thresholds" ), MimirConfig( name="pile_cc", subsets=["ngram_7_0.2", "ngram_13_0.2", "ngram_13_0.8"], description="This split contains data from the Pile's Pile CC subset at various n-gram overlap thresholds" ), MimirConfig( name="pubmed_central", subsets=["ngram_7_0.2", "ngram_13_0.2", "ngram_13_0.8"], description="This split contains data from the Pile's PubMed Central subset at various n-gram overlap thresholds" ), MimirConfig( name="wikipedia_(en)", subsets=["ngram_7_0.2", "ngram_13_0.2", "ngram_13_0.8"], description="This split contains data from the Pile's Wikipedia subset at various n-gram overlap thresholds" ), ] def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features({ "input": datasets.Value("string"), "label": datasets.Value("int32"), }), supervised_keys=None, homepage=_HOMEPAGE, citation=_CITATION, ) def _split_generators(self, dl_manager: DownloadManager): """Returns SplitGenerators.""" parent_dir = "cache_100_200_1000_512" if len(self.config.subsets) > 0: suffixes = [f"{subset}" for subset in self.config.subsets] else: suffixes = ["none"] file_paths = {} for subset_split_suffix in suffixes: internal_fp = {} subset_split_suffix_use = f"_{subset_split_suffix}" if subset_split_suffix != "none" else "" internal_fp['member'] = os.path.join(parent_dir, "train", f"{self.config.name}{subset_split_suffix_use}.jsonl") internal_fp['nonmember'] = os.path.join(parent_dir, "test", f"{self.config.name}{subset_split_suffix_use}.jsonl") file_paths[subset_split_suffix] = internal_fp # Download data data_dir = {} for k, v_dict in file_paths.items(): download_paths = [_DOWNLOAD_URL + v for v in v_dict.values()] paths = dl_manager.download_and_extract(download_paths) internal_dict = {k: v for k, v in zip(v_dict.keys(), paths)} data_dir[k] = internal_dict splits = [SplitGenerator(name=k, gen_kwargs={"file_path_dict": data_dir[k]}) for k in suffixes] return splits def _generate_examples(self, file_path_dict): """Yields individual examples for members and non-members.""" with open(file_path_dict["member"], "r") as f_member, open(file_path_dict["nonmember"], "r") as f_nonmember: for id, (member, nonmember) in enumerate(zip(f_member, f_nonmember)): member_text = json.loads(member) nonmember_text = json.loads(nonmember) # Yield separate examples for members and non-members yield f"{id}_member", { "input": member_text, "label": 1, # Member example } yield f"{id}_nonmember", { "input": nonmember_text, "label": 0, # Non-member example }