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README.md
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num_bytes: 110348975.77567595
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num_examples: 46690
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download_size: 212373125
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dataset_size: 551740152
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configs:
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- config_name: default
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data_files:
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path: data/dev-*
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- split: test
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path: data/test-*
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---
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num_bytes: 110348975.77567595
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num_examples: 46690
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download_size: 212373125
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dataset_size: 551740152
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configs:
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- config_name: default
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data_files:
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path: data/dev-*
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- split: test
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path: data/test-*
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license: mit
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task_categories:
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- sentence-similarity
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- text-classification
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language:
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- en
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size_categories:
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- 100K<n<1M
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---
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# WANDS - Wayfair ANnotation Dataset: Dataset for product search relevance assessment
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- Original source of the data is: https://github.com/wayfair/WANDS
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- Train, dev, test split of 3:1:1 as per footnote 5 in https://arxiv.org/abs/2307.00370
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## Details
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* 42,994 candidate products
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* 480 queries
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* 233,448 (query,product) relevance judgements
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## Column details
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* Product columns:
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* product_id - ID of a product
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* product_name - String of product name
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* product_class - Category which product falls under
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* category_hierarchy - Parent categories of product, delimited by ```/```
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* product_description - String description of product
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* product_features - ```|``` delimited string of attribute:value pairs which describe the product
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* rating_count - Number of user ratings for product
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* average_rating - Average rating the product received
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* review_count - Number of user reviews for product
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* Search queries columns:
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* query_id - unique ID for each query
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* query - query string
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* query_class - category to which the query falls under
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* Annotated (product,relevance judgement) pairs, columns:
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* id - Unique ID for each annotation
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* label - Relevance label, one of 'Exact', 'Partial', or 'Irrelevant'
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