To make your model more robust to minor variations like punctuation or casing, focus on preprocessing, model selection, and post-processing adjustments. Here's how to approach each step:
1. Normalize Inputs with Preprocessing Start by standardizing the text before feeding it to the model. For example:
- Convert all text to lowercase to eliminate casing differences (e.g., "Hello" → "hello").
- Remove or standardize punctuation (e.g., strip commas, periods, or replace emojis with placeholders).
- Trim extra spaces or replace multiple spaces with a single one.
Tools like Python’s re library or NLP frameworks (e.g., spaCy) can automate this. For instance, preprocessing "The Model's Output..." to "the models output" reduces surface-level noise. However, avoid over-normalization (e.g., removing apostrophes in contractions like "don't" could hurt meaning).
2. Use Models Designed for Semantic Robustness Not all models handle syntactic noise equally. Consider:
- Sentence Transformers (e.g.,
all-mpnet-base-v2): These encode entire sentences into embeddings, emphasizing semantic meaning over syntax. - Fine-Tuning with Augmented Data: If training your own model, augment training data with variations (e.g., add random punctuation/casing). For example, generate pairs like ("Hello world", "hello world!") and train the model to treat them as identical.
- Contrastive Learning: Use loss functions like Triplet Loss to minimize embedding distance between semantically similar but syntactically different examples.
Avoid models overly tuned for syntactic tasks (e.g., grammar correction) unless retrained for robustness.
3. Post-Process Embeddings Adjust how embeddings are compared:
- L2 Normalization: Scale embeddings to unit length so cosine similarity depends only on direction, not magnitude. This reduces sensitivity to minor embedding variations.
- Thresholding: Apply a score threshold (e.g., treat scores above 0.9 as "similar") to absorb small differences.
- Dimensionality Reduction: Use PCA or UMAP to focus on the most significant embedding dimensions, which may ignore noise from minor variations.
For example, after normalization, "Hello?" and "hello" might have a cosine similarity of 0.98 instead of 0.85, making scores less volatile.
Implementation Example
from sentence_transformers import SentenceTransformer
import numpy as np
# Preprocess: lowercase, remove punctuation
def preprocess(text):
return text.lower().translate(str.maketrans("", "", "!?,.:;"))
model = SentenceTransformer("all-mpnet-base-v2")
text1 = preprocess("Hello, WORLD!")
text2 = preprocess("hello world")
# Get & normalize embeddings
embedding1 = model.encode(text1)
embedding2 = model.encode(text2)
embedding1 /= np.linalg.norm(embedding1) # L2 normalize
embedding2 /= np.linalg.norm(embedding2)
similarity = np.dot(embedding1, embedding2) # Cosine similarity
print(similarity) # Likely >0.95 despite original differences
By combining these strategies, you’ll reduce sensitivity to trivial changes while preserving semantic understanding.