Update README.md

README.md

@@ -98,4 +98,86 @@ configs:
     path: sources/train-*
   - split: test
     path: sources/test-*
+license: apache-2.0
+task_categories:
+- text-classification
+- token-classification
+- text-generation
+- text2text-generation
+- sentence-similarity
+language:
+- en
+- ja
+- es
+tags:
+- amazon
+- retrieval
+- search
+- ecommerce
+- ranking
+- reranking
+size_categories:
+- 1M<n<10M
 ---
+
+# Amazon Shopping Queries Dataset
+
+A comprehensive 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
+
+## 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 NEXT%" 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}
+}
+```