README.md

---
dataset_info:
- config_name: metadata
  features:
  - name: asin
    dtype: string
  - name: title
    dtype: string
  - name: description
    dtype: string
  - name: brand
    dtype: string
  - name: main_cat
    dtype: string
  - name: category
    sequence: string
  - name: also_buy
    sequence: string
  - name: also_view
    sequence: string
  - name: imageURL
    sequence: string
  - name: imageURLHighRes
    sequence: string
  splits:
  - name: train
    num_bytes: 632329524
    num_examples: 633875
  download_size: 274315827
  dataset_size: 632329524
- config_name: reviews
  features:
  - name: reviewerID
    dtype: string
  - name: reviewerName
    dtype: string
  - name: overall
    sequence: int64
  - name: reviewTime
    sequence: timestamp[us]
  - name: asin
    sequence: string
  - name: reviewText
    sequence: string
  - name: summary
    sequence: string
  splits:
  - name: train
    num_bytes: 754819358
    num_examples: 283686
  download_size: 417425618
  dataset_size: 754819358
configs:
- config_name: metadata
  data_files:
  - split: train
    path: metadata/train-*
- config_name: reviews
  data_files:
  - split: train
    path: reviews/train-*
---
# Amazon Toys & Games Dataset

## Directory Structure

- **metadata**: Contains product information.
- **reviews**: Contains user reviews about the products.
- **filtered**:
    - **e5-base-v2_embeddings.jsonl**: Contains "asin" and "embeddings" created with [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2).
    - **metadata.jsonl**: Contains "asin" and "text", where text is created from the title, description, brand, main category, and category.
    - **reviews.jsonl**: Contains "reviewerID", "reviewTime", and "asin". Reviews are filtered to include only perfect 5-star ratings with a minimum of 5 ratings.

## Usage

### Download metadata
```python
metadata = load_dataset(path="smartcat/Amazon_Toys_and_Games_2018", name="metadata", split="train")
```

### Download reviews
```python
metadata = load_dataset(path="smartcat/Amazon_Toys_and_Games_2018", name="reviews", split="train")
```

### Download filtered files
```
filtered_reviews = load_dataset(
    path="smartcat/Amazon_Toys_and_Games_2018",
    data_files="filtered/reviews.jsonl",
    split="train",
)
```
**📎 Note:** You can set any file or list of files from the "filtered" directory as the "data_files" argument.