Update README.md

README.md

@@ -94,4 +94,70 @@ size_categories:
   * query_class - category to which the query falls under  
 * Annotated (product,relevance judgement) pairs, columns:
   * id - Unique ID for each annotation  
-  * label - Relevance label, one of 'Exact', 'Partial', or 'Irrelevant'
+  * label - Relevance label, one of 'Exact', 'Partial', or 'Irrelevant'
+
+# Citation
+
+Please cite this paper if you are building on top of or using this dataset:
+
+```text
+@InProceedings{wands,  
+  title = {WANDS: Dataset for Product Search Relevance Assessment},  
+  author = {Chen, Yan and Liu, Shujian and Liu, Zheng and Sun, Weiyi and Baltrunas, Linas and Schroeder, Benjamin},  
+  booktitle = {Proceedings of the 44th European Conference on Information Retrieval},  
+  year = {2022},  
+  numpages = {12}  
+}
+```
+
+
+# Code for generating dataset
+
+
+```python
+import pandas as pd
+from datasets import Dataset
+from datasets import DatasetDict, Dataset
+from datasets import ClassLabel, load_from_disk, load_dataset, concatenate_datasets
+from pathlib import Path
+
+base_path = "https://github.com/wayfair/WANDS/raw/main/dataset/"
+
+query_df = pd.read_csv(f"{base_path}/query.csv", sep='\t')
+product_df = pd.read_csv(f"{base_path}/product.csv", sep='\t')
+label_df = pd.read_csv(f"{base_path}/label.csv", sep='\t')
+
+df_dataset = label_df.merge(
+    query_df, on="query_id"
+).merge(
+    product_df, on="product_id"
+)
+
+wands_class_label_feature = ClassLabel(num_classes=3, names=["Irrelevant", "Partial", "Exact"])
+dataset = dataset.train_test_split(test_size=2/5, seed=1337)
+dev_test_dataset = dataset["test"].train_test_split(test_size=1/2, seed=1337)
+dataset = DatasetDict(
+    train=dataset["train"],
+    dev=dev_test_dataset["train"],
+    test=dev_test_dataset["test"],
+)
+"""
+DatasetDict({
+    train: Dataset({
+        features: ['id', 'query_id', 'product_id', 'label', 'query', 'query_class', 'product_name', 'product_class', 'category hierarchy', 'product_description', 'product_features', 'rating_count', 'average_rating', 'review_count'],
+        num_rows: 140068
+    })
+    dev: Dataset({
+        features: ['id', 'query_id', 'product_id', 'label', 'query', 'query_class', 'product_name', 'product_class', 'category hierarchy', 'product_description', 'product_features', 'rating_count', 'average_rating', 'review_count'],
+        num_rows: 46690
+    })
+    test: Dataset({
+        features: ['id', 'query_id', 'product_id', 'label', 'query', 'query_class', 'product_name', 'product_class', 'category hierarchy', 'product_description', 'product_features', 'rating_count', 'average_rating', 'review_count'],
+        num_rows: 46690
+    })
+})
+"""
+
+dataset.push_to_hub("napsternxg/wands")
+
+```