Update README.md

README.md

@@ -47,3 +47,87 @@ configs:
   - split: train
     path: data/train-*
 ---
+
+
+
+
+This dataset is a sample for the [`Amazon Shopping Queries Dataset`](https://github.com/amazon-science/esci-data). 
+
+This dataset contains queries for which at least 10 products are available. The products if possible are `exact` matches to the query intent, or at least `substitutes`
+It was constructed as follows:
+
+
+```
+import pandas as pd
+
+df_examples = pd.read_parquet("shopping_queries_dataset_examples.parquet")
+df_products = pd.read_parquet("shopping_queries_dataset_products.parquet")
+df_sources = pd.read_csv("shopping_queries_dataset_sources.csv")
+
+df_examples_products = pd.merge(
+    df_examples,
+    df_products,
+    how="left",
+    left_on=["product_locale", "product_id"],
+    right_on=["product_locale", "product_id"],
+)
+
+df_examples_products_source = pd.merge(
+    df_examples_products,
+    df_sources,
+    how="left",
+    left_on=["query_id"],
+    right_on=["query_id"],
+)
+
+list_hits = []
+for query_id in tqdm(list_query_id):
+    df = retrieve_products(query_id, df_examples_products_source)
+    list_len_desc = []
+    for row_idx in range(len(df)):
+        row = df.iloc[row_idx]
+        full_description = format_product_details(row)
+        list_len_desc.append(len(full_description))
+    if len(df) >= 10:
+        list_hits.append((df, np.mean(list_len_desc)))
+
+# sort by length of full_description
+list_hits = sorted(list_hits, key=lambda x: x[1], reverse=True)
+
+df = pd.concat([x[0] for x in list_hits[:1000]])
+```
+
+
+The auxiliary functions are:
+
+```
+def format_product_details(product):
+    template = "List of features:\n{features}\n\nDescription:\n{description}"
+    features = product["product_bullet_point"]
+    description = product["product_description"]
+    return template.format(features=features, description=description)
+
+
+def retrieve_products(query_id, df_examples_products_source):
+    df = df_examples_products_source[
+        df_examples_products_source["query_id"] == query_id
+    ]
+    # product_locale = en
+    df = df[df["product_locale"] == "us"]
+    # remove esci_label I
+    df = df[df["esci_label"] != "I"]
+    # remove product_description None
+    df = df[df["product_description"].notnull()]
+    # remove product_bullet_point None
+    df = df[df["product_bullet_point"].notnull()]
+    # if esci_label E > 10, use only those
+    if df[df["esci_label"] == "E"].shape[0] > 10:
+        df = df[df["esci_label"] == "E"]
+    # if esci_label in [E, S ]> 10, use only those
+    elif df[df["esci_label"].isin(["E", "S"])].shape[0] > 10:
+        df = df[df["esci_label"].isin(["E", "S
+    else:
+        return []
+    return df
+
+```