Update README.md

README.md

@@ -63,11 +63,11 @@ This product search dataset compiles multiple open source product search dataset
 ### Sources
 | Dataset | Repo ID | Source |
 |-------------|---------|--------|
-| Google | Marqo/marqo-GS-10M | Google Shopping |
-| Amazon | tasksource/esci | Amazon ESCI |
-| Wayfair | napsternxg/wands | Wayfair |
-| Home Depot | bstds/home_depot | Home Depot |
-| Crowdflower | napsternxg/kaggle_crowdflower_ecommerce_search_relevance | Crowdflower |
+| Google | [Marqo/marqo-GS-10M](https://huggingface.co/datasets/Marqo/marqo-GS-10M) | Google Shopping |
+| Amazon | [tasksource/esci](https://huggingface.co/datasets/tasksource/esci) | Amazon ESCI |
+| Wayfair | [napsternxg/wands](https://huggingface.co/datasets/napsternxg/wands) | Wayfair |
+| Home Depot | [bstds/home_depot](https://huggingface.co/datasets/bstds/home_depot) | Home Depot |
+| Crowdflower | [napsternxg/kaggle_crowdflower_ecommerce_search_relevance](https://huggingface.co/datasets/napsternxg/kaggle_crowdflower_ecommerce_search_relevance) | Crowdflower |
 
 ### Schema
 
@@ -104,6 +104,10 @@ Relevance: The relevance of the `<query, document>` pair.
 Each individual source will have their logic for sampling queries, documents, and relevance assessments.
 Most of the sources and manually graded by a group of annotators, except for `Marqo/marqo-GS-10M` which is the top 100 products retrieved from the system. I recommend reading the individual sources for a deeper understanding of their methodology.
 
+This format undergoes no filtering, and all `<query, document, relevance>` scores are maintained from the original source. 
+These can be directly used for training sentence similarity tasks that uses `<sentence 1, sentence 2, score>`.
+The scores should generally follow the range of 0-3, normalized across sources, but are not fully calibrated for the individual distributions.
+
 
 ### Triplets