Impara Working with Datasets and Splits | Preparing Data and Tokenization

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When preparing data for NLP tasks, you often need to load a dataset, split it into training and validation sets, and access the label information for downstream tasks such as classification. The Hugging Face datasets library provides a convenient interface for these steps, allowing you to work with popular benchmarks like IMDb, SST-2, and others. Loading a dataset is typically straightforward: you specify the dataset name and configuration, then access the splits directly or create your own custom splits. For classification problems, accessing the label column is essential for both model training and evaluation.

Note

Always shuffle and stratify your data splits. Shuffling ensures that your training and validation data are representative of the overall distribution, while stratification guarantees that class proportions are preserved across splits, leading to more balanced and reliable evaluation.


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from datasets import load_dataset
import pandas as pd

# Load a subset of the IMDb dataset (for demonstration)
dataset = load_dataset("imdb", split="train[:2000]")

# Convert to pandas DataFrame for easier manipulation
df = pd.DataFrame(dataset)

# Stratified split: preserve label distribution
from sklearn.model_selection import train_test_split

train_df, val_df = train_test_split(
    df,
    test_size=0.2,
    stratify=df["label"],
    random_state=42,
    shuffle=True
)

# Show label distribution in each split
print("Train label distribution:")
print(train_df["label"].value_counts(normalize=True))
print("\nValidation label distribution:")
print(val_df["label"].value_counts(normalize=True))

Proper splitting is crucial to avoid bias and ensure that your model's performance metrics are meaningful. If the splits are not representative, or if information from the validation set leaks into the training set, your evaluation results may be unreliable.

Note

Data leakage can occur if you split your dataset after preprocessing or tokenization, or if you inadvertently include related samples across splits. This can inflate validation performance and lead to models that do not generalize well to unseen data.

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