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Datasets:
memorilla
/
Memorilla-Data

Tasks:
Question Answering
Summarization
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
1M - 10M
Tags:
memory
long-context
personalization
retrieval
embeddings
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
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Memorilla-Data / README.md
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Document the PersonalizationV4 user ids of every split
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metadata 
pretty_name: Memorilla
language:
  - en
license: other
license_name: per-config
license_link: https://huggingface.co/datasets/memorilla/Memorilla-Data#licensing
task_categories:
  - question-answering
  - summarization
  - text-generation
tags:
  - memory
  - long-context
  - personalization
  - retrieval
  - embeddings
size_categories:
  - 1M<n<10M
configs:
  - config_name: pv4
    default: true
    data_files:
      - split: train
        path: data/pv4/train-*
      - split: validation
        path: data/pv4/validation-*
      - split: test
        path: data/pv4/test-*
  - config_name: pmv2
    data_files:
      - split: train
        path: data/pmv2/train-*
      - split: validation
        path: data/pmv2/validation-*
      - split: test
        path: data/pmv2/test-*
  - config_name: factkg
    data_files:
      - split: train
        path: data/factkg/train-*
      - split: validation
        path: data/factkg/validation-*
      - split: test
        path: data/factkg/test-*
  - config_name: triviaqa
    data_files:
      - split: train
        path: data/triviaqa/train-*
      - split: validation
        path: data/triviaqa/validation-*
      - split: test
        path: data/triviaqa/test-*
  - config_name: narrativeqa
    data_files:
      - split: train
        path: data/narrativeqa/train-*
      - split: validation
        path: data/narrativeqa/validation-*
      - split: test
        path: data/narrativeqa/test-*
  - config_name: pubmedqa
    data_files:
      - split: train
        path: data/pubmedqa/train-*
      - split: test
        path: data/pubmedqa/test-*
  - config_name: lamp4
    data_files:
      - split: train
        path: data/lamp4/train-*
      - split: test
        path: data/lamp4/test-*
  - config_name: lamp7
    data_files:
      - split: train
        path: data/lamp7/train-*
      - split: test
        path: data/lamp7/test-*
  - config_name: squad_v2
    data_files:
      - split: train
        path: data/squad_v2/train-*
      - split: validation
        path: data/squad_v2/validation-*
  - config_name: drop
    data_files:
      - split: train
        path: data/drop/train-*
      - split: validation
        path: data/drop/validation-*
  - config_name: coqa
    data_files:
      - split: train
        path: data/coqa/train-*
      - split: validation
        path: data/coqa/validation-*
  - config_name: quail
    data_files:
      - split: train
        path: data/quail/train-*
      - split: validation
        path: data/quail/validation-*
  - config_name: pwc
    data_files:
      - split: train
        path: data/pwc/train-*
      - split: validation
        path: data/pwc/validation-*
  - config_name: cnn_dailymail
    data_files:
      - split: train
        path: data/cnn_dailymail/train-*
      - split: validation
        path: data/cnn_dailymail/validation-*
  - config_name: samsum
    data_files:
      - split: train
        path: data/samsum/train-*
      - split: validation
        path: data/samsum/validation-*
  - config_name: dialogsum
    data_files:
      - split: train
        path: data/dialogsum/train-*
      - split: validation
        path: data/dialogsum/validation-*
  - config_name: msmarco
    data_files:
      - split: train
        path: data/msmarco/train-*
      - split: validation
        path: data/msmarco/validation-*
  - config_name: enwiki
    data_files:
      - split: train
        path: data/enwiki/train-*
      - split: validation
        path: data/enwiki/validation-*

Memorilla data

Training and evaluation data for Memorilla, a memory module that compresses a collection of documents into a small set of memory tokens for a frozen language model. Every row pairs a question and a reference answer with the document collection the answer should be grounded in. Precomputed Qwen3-Embedding-4B vectors for every document and every question are included, so training and evaluation start without an embedding pass.

Usage

The PersonalizationV4 benchmark is also published on its own as memorilla/PersonalizationV4.

from datasets import load_dataset

pv4 = load_dataset("memorilla/Memorilla-Data", "pv4", split="test")
row = pv4[0]
print(row["question"], row["answer"], len(row["documents"]))

The Memorilla code downloads the embeddings of a config on first use. To fetch them directly:

import torch
from huggingface_hub import snapshot_download
from safetensors import safe_open

config, split = "pv4", "test"
root = snapshot_download(
    "memorilla/Memorilla-Data",
    repo_type="dataset",
    allow_patterns=[f"embeddings/qwen3-embedding-4b/{config}/{split}/*"],
)
folder = f"{root}/embeddings/qwen3-embedding-4b/{config}/{split}"

with safe_open(f"{folder}/index.safetensors", framework="pt") as f:
    index = {key: f.get_tensor(key) for key in f.keys()}

def documents(collection_id: int):
    position = int(torch.searchsorted(index["collection_ids"], torch.tensor(collection_id)))
    shard, start, length = (int(index[k][position]) for k in ("shard", "start", "length"))
    with safe_open(f"{folder}/documents-{shard:05d}.safetensors", framework="pt") as f:
        return f.get_slice("embeddings")[start : start + length]  # [num_documents, 2560]

with safe_open(f"{folder}/questions.safetensors", framework="pt") as f:
    questions = f.get_tensor("embeddings")  # [num_rows, 2560], row-aligned with the split

print(documents(row["collection_id"]).shape, questions[0].shape)

Configs

Benchmarks

config dataset rows collection embeddings
pv4 PersonalizationV4 train 15,058 · validation 1,755 · test 4,237 user 364.5 MiB
pmv2 PersonaMem-v2 train 18,521 · validation 2,059 · test 5,000 persona 1.1 GiB
factkg FactKG train 86,367 · validation 13,266 · test 9,041 claim 69.4 GiB
triviaqa TriviaQA train 138,384 · validation 17,444 · test 500 question 124.6 GiB
narrativeqa NarrativeQA train 32,747 · validation 3,461 · test 10,557 story 11.5 GiB
pubmedqa PubMedQA train 900 · test 100 question 23.9 MiB
lamp4 LaMP-4 train 12,527 · test 1,925 question 19.1 GiB
lamp7 LaMP-7 train 10,437 · test 1,500 question 1.1 GiB

Instruction mixture

config dataset rows collection embeddings
squad_v2 SQuAD 2.0 train 86,821 · validation 5,928 question 1.2 GiB
drop DROP train 77,400 · validation 9,535 question 1.5 GiB
coqa CoQA train 108,647 · validation 7,983 question 2.5 GiB
quail QuAIL train 10,246 · validation 2,164 question 313.9 MiB
pwc PwC train 241,564 · validation 18,141 question 6.9 GiB
cnn_dailymail CNN/DailyMail train 287,113 · validation 13,368 article 14.9 GiB
samsum SAMSum train 14,731 · validation 818 conversation 190.5 MiB
dialogsum DialogSum train 12,460 · validation 500 dialogue 174.9 MiB
msmarco MS MARCO train 504,111 · validation 55,597 query 30.7 GiB

Pretraining corpus

config dataset rows collection embeddings
enwiki English Wikipedia train 1,979,809 · validation 10,017 passage 26.0 GiB
  • pv4 (PersonalizationV4). A fictional user's 200 conversations with an assistant and a scenario question about what the user would most likely do or prefer. train and validation hold the training and held-out questions of 119 users (ids 6-125, except 7); test holds every question of 30 further users (ids 126-155), none of whom appear in train or validation. memorilla/PersonalizationV4 lists the users of every split in data/user_splits.csv. hard marks 629 test questions that Qwen3-4B-Instruct-2507 gets wrong either with no documents or with only the single most relevant conversation.
  • pmv2 (PersonaMem-v2). A simulated user's conversation history and a request whose best response depends on the user's preferences. Splits follow the per-persona files of the source: test holds 5,000 questions from 200 personas. validation questions come from training personas; test personas are disjoint from train except one.
  • factkg (FactKG). A claim to label True or False together with DBpedia triples about the claim's entities, written as subject → relation → object. Original train, dev (validation) and test claims.
  • triviaqa (TriviaQA). A trivia question with the evidence documents of TriviaQA's reading-comprehension release (Wikipedia pages, or web search results when a question has none), split into passages. test is a fixed sample of 500 questions from the original validation set; validation holds the remaining 17,444. The original test questions are not included because their answers are not public.
  • narrativeqa (NarrativeQA). A question about a book or movie script; the documents are the full story text split into chunks. Original train, validation and test splits.
  • pubmedqa (PubMedQA). A biomedical research question over the sections of a PubMed abstract; the answer starts with yes, no or maybe followed by the long answer. The 1,000 expert-labeled questions (pqa_labeled), split 900 / 100 into train and test.
  • lamp4 (LaMP-4). Personalized news headline generation: an article to title, with the author's previous articles as documents. train and test are the user-based LaMP-4 train and development sets.
  • lamp7 (LaMP-7). Personalized tweet paraphrasing: a tweet to paraphrase, with the user's previous tweets as documents. train and test are the user-based LaMP-7 train and development sets.
  • squad_v2 (SQuAD 2.0). A question over a Wikipedia paragraph; only answerable questions are included. Original train and validation splits.
  • drop (DROP). A question requiring discrete reasoning (counting, arithmetic, sorting) over a paragraph. Original train and validation splits.
  • coqa (CoQA). A conversational question over a passage; the question includes the preceding turns. Original train and validation splits.
  • quail (QuAIL). A multiple-choice reading comprehension question (choices included in the question) over a passage; the answer is the text of the correct choice. Original train and validation splits.
  • pwc (PwC). An instruction about a text (questions, summaries, extraction) with the text as documents. train is the original train split; validation is the original test split.
  • cnn_dailymail (CNN/DailyMail). A news article to summarize; the answer is the article's highlights. Original train and validation splits (version 3.0.0).
  • samsum (SAMSum). A messenger-style conversation to summarize. Original train and validation splits.
  • dialogsum (DialogSum). A spoken-style dialogue to summarize. Original train and validation splits.
  • msmarco (MS MARCO). A web search query with its retrieved passages and a human-written answer (the well-formed answer when there is one); queries without an answer are not included. Original train and validation splits (v2.1).
  • enwiki (English Wikipedia). A Wikipedia passage, headed by its article title and section and split into chunks of at most about 128 tokens, with a generic instruction to restate it; the answer is the passage text. Train and validation passages.

Fields

field type description
collection_id int64 Identifies the row's document collection. Rows with the same collection_id in a split share the same documents.
question string The question or instruction.
answer string The reference answer.
choices list[string] pv4, pmv2 and factkg only. Candidate answers for embedding-based scoring; answer is one of them. pv4 and pmv2 list five candidates in A-E order, with an empty string for a missing one; factkg lists ["True", "False"].
hard bool pv4 only. Marks the hard test subset (always False in train and validation).
documents list[string] The document collection, in its original order.

Embeddings 

embeddings/qwen3-embedding-4b/<config>/<split>/
    index.safetensors            collection_ids int64 [C] (ascending), shard int32 [C], start int64 [C], length int64 [C]
    documents-XXXXX.safetensors  embeddings float16 [n, 2560]; whole collections, at most 4 GiB per shard
    questions.safetensors        embeddings float16 [R, 2560], row-aligned with data/<config>/<split>
  • Collection index["collection_ids"][i] occupies rows start[i] to start[i] + length[i] of documents-{shard[i]:05d}.safetensors, one row per entry of documents, in order.
  • Each split stores the collections it uses, so a collection shared by two splits appears in both.
  • Vectors were computed with Qwen/Qwen3-Embedding-4B in vLLM (bfloat16, default pooling, unit-normalized) without an instruction prefix and stored as float16. Questions are embedded from question.strip().

Licensing

Each config is distributed under the license of its source dataset, listed below. Some sources are restricted to non-commercial use.

config source license reference
pv4 memorilla/PersonalizationV4; persona seeds from Synthetic-Persona-Chat CC BY 4.0 Memorilla; seed personas: Jandaghi et al., 2023. Faithful Persona-based Conversational Dataset Generation with Large Language Models.
pmv2 bowen-upenn/PersonaMem-v2 CC BY 4.0 Jiang et al., 2025. PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memory.
factkg FactKG; triples from DBpedia FactKG: no license stated; DBpedia: CC BY-SA 3.0 Kim et al., 2023. FactKG: Fact Verification via Reasoning on Knowledge Graphs. ACL.
triviaqa mandarjoshi/trivia_qa (rc) Apache 2.0 Joshi et al., 2017. TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension. ACL.
narrativeqa deepmind/narrativeqa Apache 2.0 Kočiský et al., 2018. The NarrativeQA Reading Comprehension Challenge. TACL.
pubmedqa qiaojin/PubMedQA (pqa_labeled) MIT Jin et al., 2019. PubMedQA: A Dataset for Biomedical Research Question Answering. EMNLP.
lamp4 LaMP See the LaMP benchmark Salemi et al., 2024. LaMP: When Large Language Models Meet Personalization. ACL.
lamp7 LaMP See the LaMP benchmark Salemi et al., 2024. LaMP: When Large Language Models Meet Personalization. ACL.
squad_v2 rajpurkar/squad_v2 CC BY-SA 4.0 Rajpurkar et al., 2018. Know What You Don't Know: Unanswerable Questions for SQuAD. ACL.
drop ucinlp/drop CC BY-SA 4.0 Dua et al., 2019. DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs. NAACL.
coqa stanfordnlp/coqa Per-domain licenses of CoQA (CC BY-SA 4.0, MSR-LA, RACE, Apache 2.0) Reddy et al., 2019. CoQA: A Conversational Question Answering Challenge. TACL.
quail textmachinelab/quail CC BY-NC-SA 4.0 Rogers et al., 2020. Getting Closer to AI Complete Question Answering: A Set of Prerequisite Real Tasks. AAAI.
pwc sggetao/PwC Apache 2.0 Ge et al., 2024. In-context Autoencoder for Context Compression in a Large Language Model. ICLR.
cnn_dailymail abisee/cnn_dailymail Apache 2.0 See et al., 2017. Get To The Point: Summarization with Pointer-Generator Networks. ACL.
samsum knkarthick/samsum CC BY-NC-ND 4.0 Gliwa et al., 2019. SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization.
dialogsum knkarthick/dialogsum CC BY-NC-SA 4.0 Chen et al., 2021. DialogSum: A Real-Life Scenario Dialogue Summarization Dataset. Findings of ACL.
msmarco microsoft/ms_marco (v2.1) MS MARCO terms (non-commercial research use) Nguyen et al., 2016. MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.
enwiki English Wikipedia (December 2021 dump) CC BY-SA 4.0 Wikipedia contributors.

Citation

If you use this data, please cite Memorilla (see the code repository) and the original dataset of each config you use (table above).