README.md

metadata

configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: label
      dtype: int64
    - 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: 0
  dataset_size: 181542515

Amazon Polarity 10pct

This is a direct subset of the original 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

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)