Augmentation Preview
Preview text data augmentation techniques for AI training
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What is Text Data Augmentation?
Text data augmentation artificially expands your training dataset by creating variations of existing examples. This technique helps prevent overfitting, improves model generalization, and is especially valuable when you have limited labeled data.
This augmentation preview tool lets you experiment with different augmentation techniques and see how they transform your text. Use it to understand augmentation effects before applying them to your full dataset.
Augmentation Techniques Explained
Synonym Replacement
Replace words with synonyms from a dictionary or word embeddings. Preserves meaning while varying vocabulary.
Random Insertion
Insert random words (often synonyms of existing words) at random positions. Adds noise while keeping context.
Random Deletion
Remove random words with a probability. Teaches models to work with incomplete information.
Random Swap
Swap adjacent word positions. Tests model robustness to word order variations.
Best Practices for Augmentation
- Start conservative: Begin with 1-2 techniques and low augmentation rates. Aggressive augmentation can introduce noise.
- Preserve labels: For classification tasks, ensure augmented text still belongs to the original class.
- Balance the dataset: Use augmentation to oversample minority classes and address class imbalance.
- Test on validation set: Evaluate whether augmentation improves or hurts your validation metrics.
- Combine with other techniques: Augmentation works well with techniques like back-translation and paraphrasing.
When to Use Each Technique
| Technique | Best For | Caution |
|---|---|---|
| Synonym | Vocabulary robustness | May change subtle meanings |
| Insertion | Noise tolerance | Can make text ungrammatical |
| Deletion | Partial information handling | May remove key words |
| Swap | Word order flexibility | Disruptive for short texts |
Frequently Asked Questions
How much augmentation should I use?
A common approach is 2-4x the original dataset size. More augmentation helps with small datasets; large datasets benefit less.
Does augmentation work for all NLP tasks?
It works best for classification and NER. For generation tasks, be careful—augmented text may introduce errors the model learns to reproduce.