---
dataset_info:
  features:
  - name: example_id
    dtype: int64
  - name: query
    dtype: string
  - name: query_id
    dtype: int64
  - name: product_id
    dtype: string
  - name: product_locale
    dtype: string
  - name: esci_label
    dtype: string
  - name: small_version
    dtype: int64
  - name: large_version
    dtype: int64
  - name: split
    dtype: string
  - name: product_title
    dtype: string
  - name: product_description
    dtype: string
  - name: product_bullet_point
    dtype: string
  - name: product_brand
    dtype: string
  - name: product_color
    dtype: string
  - name: source
    dtype: string
  - name: full_description
    dtype: string
  - name: Boost Product Index
    dtype: int64
  - name: description
    dtype: string
  splits:
  - name: train
    num_bytes: 130485155
    num_examples: 13985
  download_size: 58648377
  dataset_size: 130485155
configs:
- config_name: default
  data_files:
  - 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

```