amazon/AmazonQAC

767 downloads in 30 days19 likesLast modified 19 Nov 2024View on Hugging Face

The checks could not identify a recognised licence ID in the dataset's metadata.

Declared licence: cdla-permissive-2.0, from the tagEvidence: README.md at 975cbfa
Licence files: No licence file at the top level of the repositoryEvidence: file list at 975cbfa
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Citation text from the card

@inproceedings{everaert-etal-2024-amazonqac,
    title = "{A}mazon{QAC}: A Large-Scale, Naturalistic Query Autocomplete Dataset",
    author = "Everaert, Dante  and
      Patki, Rohit  and
      Zheng, Tianqi  and
      Potts, Christopher",
    editor = "Dernoncourt, Franck  and
      Preo{\c{t}}iuc-Pietro, Daniel  and
      Shimorina, Anastasia",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2024",
    address = "Miami, Florida, US",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-industry.78",
    pages = "1046--1055",
    abstract = "Query Autocomplete (QAC) is a critical feature in modern search engines, facilitating user interaction by predicting search queries based on input prefixes. Despite its widespread adoption, the absence of large-scale, realistic datasets has hindered advancements in QAC system development. This paper addresses this gap by introducing AmazonQAC, a new QAC dataset sourced from Amazon Search logs, comprising 395M samples. The dataset includes actual sequences of user-typed prefixes leading to final search terms, as well as session IDs and timestamps that support modeling the context-dependent aspects of QAC. We assess Prefix Trees, semantic retrieval, and Large Language Models (LLMs) with and without finetuning. We find that finetuned LLMs perform best, particularly when incorporating

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dataset_licence({ "id": "amazon/AmazonQAC" })
signal: "unclear"
stated_licence: "cdla-permissive-2.0"
sources: 0