Principle:Norrrrrrr lyn WAInjectBench Text Feature Extraction
| Knowledge Sources | |
|---|---|
| Domains | NLP, Feature_Engineering |
| Last Updated | 2026-02-14 16:00 GMT |
Overview
A batch encoding step that transforms raw text strings into dense embedding vectors suitable for classifier training.
Description
Text Feature Extraction applies a pre-trained sentence embedding model to a batch of text strings, producing a dense feature matrix. The model processes texts in configurable batch sizes for memory efficiency. The resulting embeddings capture semantic content and serve as the feature representation for the downstream LogisticRegression classifier.
Usage
Use this after loading training data and initializing the embedding model. It bridges the data loading step and the classifier training step.
Theoretical Basis
# Batch text encoding
embeddings = embedder.encode(texts, batch_size=32)
# Result: np.ndarray of shape (N, 384)
Each text is independently tokenized, passed through the transformer, and mean-pooled to produce a single vector.
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