region:us
---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: label
    dtype:
      class_label:
        names:
          '0': negative
          '1': positive
  - name: title
    dtype: string
  - name: content
    dtype: string
  splits:
  - name: train
    num_bytes: 163359702
    num_examples: 360000
  - name: test
    num_bytes: 18182813
    num_examples: 40000
  download_size: 120691417
  dataset_size: 181542515
---

# Amazon Polarity 10pct

This is a direct subset of the original [Amazon Polarity](https://huggingface.co/datasets/amazon_polarity) dataset, downsampled 10pct with a random shuffle

### Dataset Summary

For quicker testing on Amazon Polarity. See https://huggingface.co/datasets/amazon_polarity for details and attributions


### Source Data
```python
from datasets import ClassLabel, Dataset, DatasetDict, load_dataset

ds_full = load_dataset("amazon_polarity", streaming=True)
ds_train_10_pct = Dataset.from_list(list(ds_full["train"].shuffle(seed=42).take(360_000)))
ds_test_10_pct = Dataset.from_list(list(ds_full["test"].shuffle(seed=42).take(40_000)))

ds_10_pct = DatasetDict({"train": ds_train_10_pct, "test": ds_test_10_pct})
# Need to recreate the class labels
class_label = ClassLabel(num_classes=2, names=["negative", "positive"])
ds_10_pct = ds_10_pct.map(lambda row: {"title": row["title"], "content": row["content"], "label": "negative" if not row["label"] else "positive"})
ds_10_pct = ds_10_pct.cast_column("label", class_label)
```

