Datasets:
File size: 2,678 Bytes
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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) |