Train Validation Test Splits For Text

Why this matters

In real NLP projects, a good split prevents data leakage and gives you honest performance. As an NLP Engineer, you will:

• Benchmark models (e.g., sentiment, intent, NER) with reliable metrics.
• Tune hyperparameters without overfitting to the test set.
• Handle tricky text scenarios: duplicates, multiple messages from one user, time-ordered data, and multi-label tasks.

Concept explained simply

Think of your data as three rooms:

• Train: where the model learns patterns.
• Validation: where you pick settings and compare models.
• Test: the final judge you see once, at the end.

Mental model: three doors. Data can go in one door only. Never move it between rooms later. If any person (group), duplicate, or future event can be in two rooms, your evaluation becomes overly optimistic.

Data splitting strategies for text

• Stratified by label: default for classification; keeps class proportions stable across splits.
• Group-aware: when multiple texts belong to a shared source (user, thread, document). Use this to avoid near-duplicates across splits.
• Time-based: when data drifts over time (news, social media, support tickets). Train on past, validate on recent past, test on future.
• Multi-label aware: preserve label co-occurrences (iterative stratification is helpful conceptually).
• Deduplicate before splitting: remove exact and near-duplicate texts or ensure duplicates stay within the same split (via grouping).

How to size your splits

• Common defaults: 70% train, 15% validation, 15% test.
• Very small data: 80% train, 10% validation, 10% test; consider k-fold cross-validation for model selection, plus a small hold-out test.
• Streaming/time-ordered: allocate the latest 15–20% as test, the previous 10–15% as validation, and the rest as train.

Worked examples

Example 1: Binary sentiment with duplicates (stratified + dedup)

1. Inspect labels: Positive=60%, Negative=40%.
2. Deduplicate exact repeats (e.g., same review text) first.
3. Perform a stratified split: 70%/15%/15% maintaining label ratios.

# 1) Remove duplicates by normalized text
texts = dedupe(texts, key=lambda t: t.lower().strip())
labels = align_labels(texts)

# 2) Split: train vs temp
X_train, X_temp, y_train, y_temp = train_test_split(
    texts, labels, test_size=0.30, stratify=labels, random_state=42)

# 3) Split temp into val vs test
X_val, X_test, y_val, y_test = train_test_split(
    X_temp, y_temp, test_size=0.50, stratify=y_temp, random_state=42)

Example 2: Intent classification with multiple texts per user (group-aware)

1. Group key: user_id.
2. Use group-aware splitting to keep all messages from one user in a single split.
3. Within each split, check label balance; adjust sizes if needed.

# Group-aware split example (conceptual)
from sklearn.model_selection import GroupShuffleSplit

splitter = GroupShuffleSplit(test_size=0.30, n_splits=1, random_state=42)
train_idx, temp_idx = next(splitter.split(texts, labels, groups=user_ids))

# Now split temp into val/test with groups again
valtest_groups = [user_ids[i] for i in temp_idx]
texts_temp = [texts[i] for i in temp_idx]
labels_temp = [labels[i] for i in temp_idx]

splitter2 = GroupShuffleSplit(test_size=0.50, n_splits=1, random_state=42)
val_idx_rel, test_idx_rel = next(splitter2.split(texts_temp, labels_temp, groups=valtest_groups))

Example 3: News topic classification over time (time-based)

1. Sort by publish_date ascending.
2. Train: earliest 70%, Validation: next 15%, Test: final 15% (future).
3. Expect slightly lower but more realistic test performance due to drift.

# Sort by time and slice
records = sorted(records, key=lambda r: r.date)
N = len(records)
train = records[:int(0.70*N)]
val = records[int(0.70*N):int(0.85*N)]
test = records[int(0.85*N):]

Prevent leakage: quick checklist

• Remove duplicates and near-duplicates (normalize case/whitespace; consider simple similarity for near-dupes).
• Group by entity when texts share an origin (user_id, document_id, thread_id).
• For time data, never let future samples appear in train or validation.
• Keep raw text cleaning identical across splits; do not fit text normalizers on all data—fit on train only.
• Do not tune on the test set. Tune on validation or via cross-validation.

Exercises you can do now

Mirror of the exercises below. You can complete them here and then open the Quick Test.

Exercise 1: Stratified split with duplicates

Dataset (id, text, label):

1, "Great movie!", positive
2, "Terrible acting.", negative
3, "Loved it", positive
4, "Bad plot.", negative
5, "Great movie!", positive  # duplicate of id=1
6, "Not my taste", negative
7, "Amazing soundtrack", positive
8, "Awful pacing", negative
9, "Superb!", positive
10, "Boring.", negative
11, "Superb!", positive      # duplicate of id=9
12, "Decent", positive

• Task: Deduplicate by text, then make a 70/15/15 stratified split. List item ids per split and show class counts.

Exercise 2: Group-aware split by author

Dataset (id, author_id, text, label):

1, A, "Refund not received", refund
2, A, "Where is my refund?", refund
3, B, "App keeps crashing", tech
4, C, "Charges look wrong", billing
5, B, "Crash on login", tech
6, D, "How to change plan?", info
7, C, "Incorrect invoice", billing
8, E, "Refund timeline?", refund

• Task: Split into 70% train, 15% val, 15% test using author_id as group. No author should appear in multiple splits. Show ids per split and label counts.

Common mistakes and how to self-check

• Leaking duplicates: If the same or near-identical sentence appears across splits, your metrics will be inflated.
• Ignoring groups: Utterances from the same user in train and validation create overoptimistic results.
• Randomly splitting time series: Mixing future with past yields unrealistic performance.
• Peeking at the test: Adjusting anything after seeing test metrics invalidates the test.
• Unstratified splits on imbalanced data: The minority class may disappear from validation/test, making metrics unstable.

Practical projects

• Customer Support Intents: Build a dataset from tickets with user_id groups; perform group-aware 70/15/15 split; train a baseline classifier; report stratified metrics.
• News Topic Drift: Time-split by month; fine-tune a text classifier; compare validation vs future test performance and document drift.
• Product Reviews: Detect and handle duplicates; perform stratified split; demonstrate how leakage changes accuracy by intentionally creating a leaky split (for learning only).

Mini challenge

You have 50k tweets over 6 months for hate-speech detection. Many users post multiple times, and the platform changes in month 6.

• Choose a splitting strategy and justify it in 3 sentences.
• List one leakage risk and how you will mitigate it.
• Specify your chosen split sizes.

Who this is for

• Junior to mid-level NLP Engineers aiming for reliable model evaluation.
• Data scientists preparing text datasets for supervised learning.
• ML engineers integrating NLP models into production.

Prerequisites

• Basic Python and familiarity with train/validation/test concepts.
• Understanding of text datasets and labels (single- or multi-label).
• Awareness of class imbalance and data leakage.

Learning path

• Before this: Label definitions and quality checks; text normalization basics.
• This lesson: Robust splitting strategies for text.
• Next: Feature extraction/tokenization and baseline modeling; then error analysis.

Next steps

• Document your split rationale and freeze the seed/splits for reproducibility.
• Run baseline models and compare on the validation split only.
• When ready, evaluate once on the test split and record results.

Quick test note

The quick test is available to everyone; only logged-in users get saved progress.