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imperial-cpg
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pile_arxiv_doc_mia

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matthieumeeus97 commited on Oct 7

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@@ -18,6 +18,10 @@ This dataset contains **full** ArXiv papers randomly sampled from the train (mem
 We randomly sample 1,000 documents members and 1,000 non-members, ensuring that the selected documents have at least 5,000 words (any sequences of characters seperated by a white space).
 We also provide the dataset where each document is split into 25 sequences of 200 words [here](https://huggingface.co/datasets/imperial-cpg/pile_arxiv_doc_mia_sequences).
 The dataset can be used to develop and evaluate document-level MIAs against LLMs trained on The Pile.
 Target models include the suite of Pythia and GPTNeo models, to be found [here](https://huggingface.co/EleutherAI). Our understanding is that the deduplication executed on the Pile to create the "Pythia-dedup" models has been only done on the training dataset, suggesting this dataset of members/non-members also to be valid for these models.

 We randomly sample 1,000 documents members and 1,000 non-members, ensuring that the selected documents have at least 5,000 words (any sequences of characters seperated by a white space).
 We also provide the dataset where each document is split into 25 sequences of 200 words [here](https://huggingface.co/datasets/imperial-cpg/pile_arxiv_doc_mia_sequences).
+The dataset contains as columns:
+- text: the raw text of the sequence
+- label: binary label for membership (1=member)
 The dataset can be used to develop and evaluate document-level MIAs against LLMs trained on The Pile.
 Target models include the suite of Pythia and GPTNeo models, to be found [here](https://huggingface.co/EleutherAI). Our understanding is that the deduplication executed on the Pile to create the "Pythia-dedup" models has been only done on the training dataset, suggesting this dataset of members/non-members also to be valid for these models.