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---
dataset_info:
features:
- name: example_id
dtype: int64
- name: query
dtype: string
- name: query_id
dtype: int64
- name: product_id
dtype: string
- name: product_locale
dtype: string
- name: esci_label
dtype: string
- name: small_version
dtype: int64
- name: large_version
dtype: int64
- name: product_title
dtype: string
- name: product_description
dtype: string
- name: product_bullet_point
dtype: string
- name: product_brand
dtype: string
- name: product_color
dtype: string
- name: product_text
dtype: string
splits:
- name: train
num_bytes: 5047037946
num_examples: 2027874
- name: test
num_bytes: 1631847321
num_examples: 652490
download_size: 2517788457
dataset_size: 6678885267
license: apache-2.0
task_categories:
- text-classification
- text-retrieval
language:
- en
- ja
- es
---
# Dataset Card for "esci"
ESCI product search dataset
https://github.com/amazon-science/esci-data/
Preprocessings:
-joined the two relevant files
-product_text aggregate all product text
-mapped esci_label to full name
```bib
@article{reddy2022shopping,
title={Shopping Queries Dataset: A Large-Scale {ESCI} Benchmark for Improving Product Search},
author={Chandan K. Reddy and Lluís Màrquez and Fran Valero and Nikhil Rao and Hugo Zaragoza and Sambaran Bandyopadhyay and Arnab Biswas and Anlu Xing and Karthik Subbian},
year={2022},
eprint={2206.06588},
archivePrefix={arXiv}
}
``` |