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--- |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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dataset_info: |
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features: |
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- name: label |
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dtype: int64 |
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- name: title |
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dtype: string |
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- name: content |
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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 |
|
--- |
|
|
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# Amazon Polarity 10pct |
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This is a direct subset of the original [Amazon Polarity](https://huggingface.co/datasets/amazon_polarity) dataset, downsampled 10pct with a random shuffle |
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|
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### Dataset Summary |
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|
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For quicker testing on Amazon Polarity. See https://huggingface.co/datasets/amazon_polarity for details and attributions |
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### Source Data |
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```python |
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from datasets import ClassLabel, Dataset, DatasetDict, load_dataset |
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|
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ds_full = load_dataset("amazon_polarity", streaming=True) |
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ds_train_10_pct = Dataset.from_list(list(ds_full["train"].shuffle(seed=42).take(360_000))) |
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ds_test_10_pct = Dataset.from_list(list(ds_full["test"].shuffle(seed=42).take(40_000))) |
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|
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ds_10_pct = DatasetDict({"train": ds_train_10_pct, "test": ds_test_10_pct}) |
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# Need to recreate the class labels |
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class_label = ClassLabel(num_classes=2, names=["negative", "positive"]) |
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ds_10_pct = ds_10_pct.map(lambda row: {"title": row["title"], "content": row["content"], "label": "negative" if not row["label"] else "positive"}) |
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ds_10_pct = ds_10_pct.cast_column("label", class_label) |
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``` |
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