task_categories:text-classification task_categories:text-retrieval language:en language:ja language:es license:apache-2.0 arxiv:2206.06588 region:us
---
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}
}
```