change processing script

README.md

@@ -27,20 +27,15 @@ To load the dataset:
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset("iamgroot42/mimir", "pile_cc", split="ngram_7_0.2")
+dataset = load_dataset("Al-not-AI/mimir", "pile_cc", split="ngram_7_0.2")
 ```
 
 - Available Names: `arxiv`, `dm_mathematics`, `github`, `hackernews`, `pile_cc`, `pubmed_central`, `wikipedia_(en)`, `full_pile`, `c4`, `temporal_arxiv`, `temporal_wiki`
 - Available Splits: `ngram_7_0.2`, `ngram_13_0.2`, `ngram_13_0.8` (for most sources), 'none' (for other sources)
 - Available Features: `member` (str), `nonmember` (str), `member_neighbors` (List[str]), `nonmember_neighbors` (List[str])
 
-## 🛠️ Codebase
-For evaluating MIA methods on our datasets, visit our [GitHub repository](http://github.com/iamgroot42/mimir).
 
-
-## ⭐ Citing our Work
-
-If you find our codebase and datasets beneficial, kindly cite [our work](https://arxiv.org/pdf/2402.07841.pdf):
+This dataset is forked from a respository linked with this paper:
 
 ```bibtex
 @inproceedings{duan2024membership,
@@ -49,4 +44,6 @@ If you find our codebase and datasets beneficial, kindly cite [our work](https:/
       year={2024},
       booktitle={Conference on Language Modeling (COLM)},
 }
-```
+```
+
+The only cahange is in the processing script.

mimir.py

@@ -1,8 +1,3 @@
-"""
-    Data used for experiments with MIMIR. Processed train/test splits for models trained on the Pile (for now).
-    Processing data at HF end.
-"""
-
 from datasets import (
     GeneratorBasedBuilder,
     SplitGenerator,
@@ -21,7 +16,6 @@ _HOMEPAGE = "http://github.com/iamgroot42/mimir"
 
 _DESCRIPTION = """\
 Member and non-member splits for our MI experiments using MIMIR. Data is available for each source.
-We also cache neighbors (generated for the NE attack).
 """
 
 _CITATION = """\
@@ -39,7 +33,7 @@ _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):
+    def __init__(self, *args, subsets: List[str] = [], **kwargs):
         """Constructs a MimirConfig.
 
         Args:
@@ -50,10 +44,8 @@ class MimirConfig(BuilderConfig):
 
 
 class MimirDataset(GeneratorBasedBuilder):
-    # Assuming 'VERSION' is defined
     VERSION = datasets.Version("1.3.0")
 
-    # Define the builder configs
     BUILDER_CONFIG_CLASS = MimirConfig
     BUILDER_CONFIGS = [
         MimirConfig(
@@ -91,52 +83,23 @@ class MimirDataset(GeneratorBasedBuilder):
             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"
         ),
-        MimirConfig(
-            name="full_pile", description="This split contains data from multiple sources in the Pile",
-        ),
-        MimirConfig(
-            name="c4", description="This split contains data the C4 dataset",
-        ),
-        MimirConfig(
-            name="temporal_arxiv", 
-            subsets=["2020_08", "2021_01", "2021_06", "2022_01", "2022_06", "2023_01", "2023_06"],
-            description="This split contains benchmarks where non-members are selected from various months from 2020-08 and onwards",
-        ),
-        MimirConfig(
-            name="temporal_wiki", description="This split contains benchmarks where non-members are selected from 2023-08 and onwards",
-        ),
     ]
 
     def _info(self):
         return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
             features=datasets.Features({
-                "member": datasets.Value("string"),
-                "nonmember": datasets.Value("string"),
-                "member_neighbors": datasets.Sequence(datasets.Value("string")),
-                "nonmember_neighbors": datasets.Sequence(datasets.Value("string"))
+                "input": datasets.Value("string"),
+                "label": datasets.Value("int32"),
             }),
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
             supervised_keys=None,
-            # Homepage of the dataset for documentation
             homepage=_HOMEPAGE,
-            # Citation for the dataset
             citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager: DownloadManager):
         """Returns SplitGenerators."""
-        # Path to the data files
-        NEIGHBOR_SUFFIX = "_neighbors_25_bert_in_place_swap"
-        parent_dir = (
-            "cache_100_200_10000_512"
-            if self.config.name == "full_pile"
-            else "cache_100_200_1000_512"
-        )
+        parent_dir = "cache_100_200_1000_512"
 
         if len(self.config.subsets) > 0:
             suffixes = [f"{subset}" for subset in self.config.subsets]
@@ -149,46 +112,35 @@ class MimirDataset(GeneratorBasedBuilder):
 
             subset_split_suffix_use = f"_{subset_split_suffix}" if subset_split_suffix != "none" else ""
 
-            # Add standard member and non-member paths
             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")
 
-            # Load associated neighbors
-            internal_fp['member_neighbors'] = os.path.join(
-                parent_dir,
-                "train_neighbors",
-                f"{self.config.name}{subset_split_suffix_use}{NEIGHBOR_SUFFIX}.jsonl",
-            )
-            internal_fp['nonmember_neighbors'] = os.path.join(
-                parent_dir,
-                "test_neighbors",
-                f"{self.config.name}{subset_split_suffix_use}{NEIGHBOR_SUFFIX}.jsonl",
-            )
             file_paths[subset_split_suffix] = internal_fp
 
-        # Now that we know which files to load, download them
+        # Download data
         data_dir = {}
         for k, v_dict in file_paths.items():
-            download_paths = []
-            for v in v_dict.values():
-                download_paths.append(_DOWNLOAD_URL + v)
+            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)}
+            internal_dict = {k: v for k, v in zip(v_dict.keys(), paths)}
             data_dir[k] = internal_dict
 
-        splits = []
-        for k in suffixes:
-            splits.append(SplitGenerator(name=k, gen_kwargs={"file_path_dict": data_dir[k]}))
+        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 examples."""
-        # Open all four files in file_path_dict and yield examples (one from each file) simultaneously
-        with open(file_path_dict["member"], "r") as f_member, open(file_path_dict["nonmember"], "r") as f_nonmember, open(file_path_dict["member_neighbors"], "r") as f_member_neighbors, open(file_path_dict["nonmember_neighbors"], "r") as f_nonmember_neighbors:
-            for id, (member, nonmember, member_neighbors, nonmember_neighbors) in enumerate(zip(f_member, f_nonmember, f_member_neighbors, f_nonmember_neighbors)):
-                yield id, {
-                    "member": json.loads(member),
-                    "nonmember": json.loads(nonmember),
-                    "member_neighbors": json.loads(member_neighbors),
-                    "nonmember_neighbors": json.loads(nonmember_neighbors),
-                }
+        """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
+                }