README.md

---
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: 26826437400
        num_examples: 28941558
      - name: test
        num_bytes: 26826437400
        num_examples: 6722877
    download_size: 583323916
    dataset_size: 2399240463
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   |