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---
dataset_info:
- config_name: pairs
features:
- name: query
dtype: string
- name: document
dtype: string
- name: relevance
dtype: float64
- name: source
dtype: string
splits:
- name: train
num_bytes: 2565164850
num_examples: 5571429
- name: test
num_bytes: 730814746
num_examples: 1462128
download_size: 1234904598
dataset_size: 3295979596
- config_name: triplets
features:
- name: anchor
dtype: string
- name: positive
dtype: string
- name: negative
dtype: string
- name: margin
dtype: float64
- name: source
dtype: string
- name: metadata
dtype: string
splits:
- name: train
num_bytes: 265774223
num_examples: 955038
- name: test
num_bytes: 99868063
num_examples: 361622
download_size: 5258297
dataset_size: 365642286
configs:
- config_name: pairs
data_files:
- split: train
path: pairs/train-*
- split: test
path: pairs/test-*
- config_name: triplets
data_files:
- split: train
path: triplets/train*
- split: test
path: triplets/test*
license: apache-2.0
---
This product search dataset compiles multiple open source product search datasets, that can be used for representation learning tasks.
### Sources
| Dataset | Repo ID | Source |
|-------------|---------|--------|
| 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
### Document
To standardize attributes across different sources and their availability, we use a template that can be applied based on available product information.
```python
if kwargs.get("title"):
template = f"""**product title**: {kwargs.get('title')}\n"""
else:
template = """"""
if kwargs.get("category"):
template += f"""**product category**: {kwargs.get('category').replace(" / ", " > ")}\n"""
if kwargs.get("attributes"):
template += """**product attributes**:\n"""
for k, v in kwargs.get("attributes").items():
template += f""" - **{k}**: {v}\n"""
if kwargs.get("description"):
template += f"""**product description**: {kwargs.get('description')}"""
```
The dataset has two splits:
- `Pairs`
- `Triplets`
### Pairs
Query: The user query.
Document: The product that was retrieved by the system.
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
### Train
| Dataset | Queries | Documents | Pairs |
|-------------|---------|-----------|----------|
| Google | 77,288 | 2,202,907 | 3,926,764|
| Amazon | 99,408 | 985,476 | 1,420,372|
| Wayfair | 477 | 38,854 | 140,068 |
| Home Depot | 11,795 | 54,360 | 74,067 |
| Crowdflower | 261 | 9,912 | 10,158 |
### Test
| Dataset | Queries | Documents | Pairs |
|-------------|---------|-----------|----------|
| Google | 19,564 | 748,386 | 981,204 |
| Amazon | 30,947 | 364,004 | 434,234 |
| Wayfair | 477 | 25,317 | 46,690 | |