| --- |
| dataset_info: |
| features: |
| - name: text |
| dtype: string |
| - name: label |
| dtype: string |
| splits: |
| - name: train |
| num_examples: 196629 |
| num_bytes: 104709025 |
| - name: validation |
| num_examples: 28090 |
| - name: test |
| num_examples: 56181 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train.parquet |
| - split: validation |
| path: data/validation.parquet |
| - split: test |
| path: data/test.parquet |
| task_categories: |
| - text-classification |
| license: cc-by-nc-4.0 |
| --- |
| |
| Dataset adapted from [spawn99/wine-reviews](https://huggingface.co/datasets/spawn99/wine-reviews/blob/main/README.md) to train classifiers on grape variety. |
| Columns were consolidated to match the format described in [this project](https://github.com/ivanfioravanti/wine_variety_classification/blob/main/data_utils.py) |
| ``` |
| import polars as pl |
| from datasets import load_dataset |
| |
| ds_dict = load_dataset("spawn99/wine-reviews") |
| |
| processed_splits = {} |
| |
| for split_name, ds in ds_dict.items(): |
| print(f"Processing {split_name} split...") |
| |
| # Convert to Polars (Zero-copy via Arrow) |
| df = pl.from_arrow(ds.data.table) |
| |
| # Apply transformation logic |
| df_final = df.select([ |
| pl.format( |
| "Based on this wine review, guess the grape variety:\n" |
| "This wine is produced by {} in the {} region of {}.\n" |
| "It was grown in {}. It is described as: \"{}\".\n" |
| "The wine has been reviewed by {} and received {} points.\n" |
| "The price is {}.", |
| pl.col("winery").fill_null("a winery"), |
| # Region logic: region_1 or province or region_2 or "Unknown region" |
| pl.coalesce(["region_1", "province", "region_2"]).fill_null("Unknown region"), |
| pl.col("country").fill_null("Unknown country"), |
| pl.col("designation").fill_null("an unspecified appellation"), |
| pl.col("description").fill_null("No description provided."), |
| pl.col("taster_name").fill_null("a reviewer"), |
| pl.col("points").cast(pl.String).fill_null("unrated"), |
| # Price logic: cast to int to remove .0 then to string |
| pl.col("price").cast(pl.Int64).cast(pl.String).fill_null("unknown") |
| ).alias("text"), |
| |
| pl.col("variety").alias("label") |
| ]).filter(pl.col("label").is_not_null()) |
| |
| processed_splits[split_name] = df_final |
| |
| # Save locally or inspect |
| print(f"Split {split_name} finished. Rows: {len(df_final)}") |
| df_final.write_parquet(f"processed/{split_name}.parquet") |
| ``` |
|
|
| # Original Dataset Details |
|
|
| - **License:** [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
| - **Attribution:** Zackthoutt |
| - **Source:** [Wine Reviews Dataset on Kaggle](https://www.kaggle.com/datasets/zynicide/wine-reviews) |
