Duplicate from milistu/amazon-esci-data

Co-authored-by: Milutin Studen <milistu@users.noreply.huggingface.co>

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README.md

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+---
+dataset_info:
+- config_name: products
+  features:
+  - name: product_id
+    dtype: string
+  - 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_locale
+    dtype: string
+  - name: split
+    dtype: string
+  - name: __index_level_0__
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 1650407845
+    num_examples: 1371823
+  - name: test
+    num_bytes: 537176847
+    num_examples: 443101
+  download_size: 1149707182
+  dataset_size: 2187584692
+- config_name: queries
+  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: split
+    dtype: string
+  - name: __index_level_0__
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 198670365
+    num_examples: 1983272
+  - name: test
+    num_bytes: 63544917
+    num_examples: 638016
+  download_size: 63596052
+  dataset_size: 262215282
+- config_name: sources
+  features:
+  - name: query_id
+    dtype: int64
+  - name: source
+    dtype: string
+  - name: split
+    dtype: string
+  - name: __index_level_0__
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 3458419
+    num_examples: 99683
+  - name: test
+    num_bytes: 1048200
+    num_examples: 30969
+  download_size: 1510331
+  dataset_size: 4506619
+configs:
+- config_name: products
+  data_files:
+  - split: train
+    path: products/train-*
+  - split: test
+    path: products/test-*
+- config_name: queries
+  data_files:
+  - split: train
+    path: queries/train-*
+  - split: test
+    path: queries/test-*
+- config_name: sources
+  data_files:
+  - split: train
+    path: sources/train-*
+  - split: test
+    path: sources/test-*
+license: apache-2.0
+task_categories:
+- text-classification
+- token-classification
+- text-generation
+- sentence-similarity
+language:
+- en
+- ja
+- es
+tags:
+- amazon
+- retrieval
+- search
+- ecommerce
+- ranking
+- reranking
+size_categories:
+- 1M<n<10M
+---
+
+# Amazon Shopping Queries Dataset
+
+Dataset for improving product search, ranking and recommendations, featuring query-product pairs with detailed relevance labels.
+
+## Overview
+The dataset contains search queries paired with up to 40 potentially relevant products, each labeled using the ESCI system:
+- **E**xact match: Products that perfectly match the customer's search intent (e.g., searching "iPhone 13" and finding "Apple iPhone 13 128GB")
+- **S**ubstitute product: Alternative products that could satisfy the same need (e.g., searching "iPhone 13" and finding "iPhone 14" or "Samsung Galaxy S23")
+- **C**omplement product: Products commonly bought together with the searched item (e.g., searching "iPhone 13" and finding "iPhone 13 case" or "screen protector")
+- **I**rrelevant result: Products that don't match the customer's search intent (e.g., searching "iPhone 13" and finding "laptop charger")
+
+## Dataset Statistics
+### Reduced Version (Task 1)
+- 48,300 unique queries
+- 1,118,011 query-product pairs
+- **Focus**: Filtered to exclude "easy" queries, making it more challenging
+- Language distribution:
+  - English (US): 29,844 queries
+  - Spanish (ES): 8,049 queries
+  - Japanese (JP): 10,407 queries
+
+### Full Version (Tasks 2 & 3)
+- 130,652 unique queries
+- 2,621,738 query-product pairs
+- **Focus**: Includes both easy and challenging queries
+- Language distribution:
+  - English (US): 97,345 queries
+  - Spanish (ES): 15,180 queries
+  - Japanese (JP): 18,127 queries
+
+## Features
+- Rich product metadata including:
+  - Product title
+  - Product description
+  - Product bullet points
+  - Brand information
+  - Color information
+- Multilingual support (English, Japanese, Spanish)
+- Train/test splits for each task
+
+## Download
+Install `datasets` library:
+```bash
+pip install datasets
+```
+Donwload files:
+```python
+from datasets import load_dataset
+
+queries = load_dataset(path="milistu/amazon-esci-data", name="queries", split=["train", "test"])
+products = load_dataset(path="milistu/amazon-esci-data", name="products", split=["train", "test"])
+sources = load_dataset(path="milistu/amazon-esci-data", name="sources", split=["train", "test"])
+```
+
+## Use Cases
+1. **Product Ranking**: Develop algorithms to rank relevant products higher in search results
+2. **Relevance Classification**: Build models to classify products as Exact, Substitute, Complement, or Irrelevant
+3. **Substitute Detection**: Identify substitute products for improved product recommendations
+4. **Semantic Search**: Train embedding models (like BERT, sentence-transformers) to:
+    - Capture semantic similarity between queries and products
+    - Handle long-tail queries with no exact keyword matches
+    - Understand product relationships across categories
+    - Example: Query "comfortable running shoes for marathon" can match with "Nike Air Zoom Alphafly" even without exact keyword overlap
+
+## Citation
+Originally sourced from ["Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search"](https://github.com/amazon-science/esci-data?tab=readme-ov-file), this version is optimized for machine learning applications and semantic search research.
+```
+@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}
+}
+```

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