---
language:
- en
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: query
    dtype: string
  - name: product_title
    dtype: string
  - name: product_description
    dtype: string
  - name: median_relevance
    dtype: float64
  - name: relevance_variance
    dtype: float64
  - name: split
    dtype: string
  splits:
  - name: train
    num_bytes: 5156813
    num_examples: 10158
  - name: test
    num_bytes: 14636826
    num_examples: 22513
  download_size: 10796818
  dataset_size: 19793639
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

# Crowdflower Search Results Relevance

* Original source: https://www.kaggle.com/c/crowdflower-search-relevance/overview
* More detailed version: https://data.world/crowdflower/ecommerce-search-relevance

## Citation
```
@misc{crowdflower-search-relevance,
    author = {AaronZukoff, Anna Montoya, JustinTenuto, Wendy Kan},
    title = {Crowdflower Search Results Relevance},
    publisher = {Kaggle},
    year = {2015},
    url = {https://kaggle.com/competitions/crowdflower-search-relevance}
}
```

## Code for generating data

```python
# ! unzip train.csv.zip
# ! unzip test.csv.zip 


df_comp = pd.concat([
    pd.read_csv("./train.csv").assign(split="train"),
    pd.read_csv("./test.csv").assign(split="test"),
])

dataset = DatasetDict(
    train=Dataset.from_pandas(df_comp[df_comp["split"] == "train"].reset_index(drop=True)),
    test=Dataset.from_pandas(df_comp[df_comp["split"] == "test"].reset_index(drop=True)),
)

dataset.push_to_hub("napsternxg/kaggle_crowdflower_ecommerce_search_relevance")
```